Additional Information on Socio-Ecological Systems and Social Influence Network Modeling

Topics
Socio-Ecological Systems Adaptive Capacity and Collaborative Governance Communitity Vulnerability and Resilience Cultural Theory Social Network Modeling Model Assessment Network Analysis Self-Organizing Networks

Socio-Ecological Systems

  1. @article{article, author={Bodin, Örjan and Crona, Beatrice and Thyresson, Matilda and Golz, Anna-Lea and Tengö, Maria}, year={2014}, month={04}, pages={}, title={Conservation Success as a Function of Good Alignment of Social and Ecological Structures and Processes}, volume={28}, journal={Conservation biology : the journal of the Society for Conservation Biology}, doi={10.1111/cobi.12306} localURL={Documents/Bodin 2014.pdf}, }
  2. @article{article, author={Binder, Claudia and Hinkel, Jochen and Bots, Pieter and Pahl-Wostl, Claudia}, year={2013}, month={12}, pages={art26}, title={Comparison of Frameworks for Analyzing Social-ecological Systems}, volume={18}, journal={Ecology and Society}, doi={10.5751/ES-05551-180426} localURL={Documents/Binder et al. 2013 ES-2013-5551.pdf}, }
  3. @article{doi:10.1080/14719037.2017.1364414, author={Elizabeth Anne Eppel and Mary Lee Rhodes}, title={Complexity theory and public management: a ‘becoming’ field}, journal={Public Management Review}, volume={20}, number={7}, pages={949-959}, year ={2018}, publisher={Routledge}, doi={10.1080/14719037.2017.1364414}, URL={https://doi.org/10.1080/14719037.2017.1364414}, localURL={Documents/Teisman 2008. COMPLEXITY THEORY AND PUBLIC MANAGEMENT.pdf}, eprint={https://doi.org/10.1080/14719037.2017.1364414} }
  4. @article{article, author={Levin, Simon and Xepapadeas, Tasos and Crépin, Anne-Sophie and Norberg, Jon and Zeeuw, Aart and Folke, Carl and Hughes, Terence and Arrow, Kenneth and Barrett, Scott and Daily, Gretchen and Ehrlich, Paul and Kautsky, Nils and Mäler, Karl-Göran and Polasky, Steve and Troell, Max and Vincent, Jeffrey and Walker, Brian}, year={2013}, month={04}, pages={}, title={Social-Ecological Systems as Complex Adaptive Systems: Modeling and Policy Implications}, volume={18}, journal={Environment and Development Economics}, doi={10.1017/S1355770X12000460} localURL={Documents/Levin 2013 SES as complex_adaptive_systems_modeling_and_policy_implications.pdf}, }
  5. @article {Ostrom419, author={Ostrom, Elinor}, title={A General Framework for Analyzing Sustainability of Social-Ecological Systems}, volume={325}, number={5939}, pages={419--422}, year={2009}, doi={10.1126/science.1172133}, publisher={American Association for the Advancement of Science}, abstract={A major problem worldwide is the potential loss of fisheries, forests, and water resources. Understanding of the processes that lead to improvements in or deterioration of natural resources is limited, because scientific disciplines use different concepts and languages to describe and explain complex social-ecological systems (SESs). Without a common framework to organize findings, isolated knowledge does not cumulate. Until recently, accepted theory has assumed that resource users will never self-organize to maintain their resources and that governments must impose solutions. Research in multiple disciplines, however, has found that some government policies accelerate resource destruction, whereas some resource users have invested their time and energy to achieve sustainability. A general framework is used to identify 10 subsystem variables that affect the likelihood of self-organization in efforts to achieve a sustainable SES.}, issn={0036-8075}, URL={https://science.sciencemag.org/content/325/5939/419}, localURL={Documents/GenFrameworkForAnalyzingSustainabiltyOfSESs.pdf}, journal={Science} }
  6. @article{, title="Resilience, Adaptability and Transformability in Social–ecological Systems", journal="Ecology and Society", volume="9", number="2", pages="5", year="2004", localURL="Documents/ResilienceAdaptabilityTransformabilityInSESs.pdf", author="B. Walker and C. S. Holling and S. Carpenter and A. Kinzig", abstract="The concept of resilience has evolved considerably since Holling’s (1973) seminal paper. Different interpretations of what is meant by resilience, however, cause confusion. Resilience of a system needs to be considered in terms of the attributes that govern the system’s dynamics. Three related attributes of social– ecological systems (SESs) determine their future trajectories: resilience, adaptability, and transformability. Resilience (the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks) has four components—latitude, resistance, precariousness, and panarchy—most readily portrayed using the metaphor of a stability landscape. Adaptability is the capacity of actors in the system to influence resilience (in a SES, essentially to manage it). There are four general ways in which this can be done, corresponding to the four aspects of resilience. Transformability is the capacity to create a fundamentally new system when ecological, economic, or social structures make the existing system untenable. The implications of this interpretation of SES dynamics for sustainability science include changing the focus from seeking optimal states and the determinants of maximum sustainable yield (the MSY paradigm), to resilience analysis, adaptive resource management, and adaptive governance. " }
  7. @InProceedings{10.1007/978-94-024-1123-2_3, author="Seager, Thomas P. and Clark, Susan Spierre and Eisenberg, Daniel A. and Thomas, John E. and Hinrichs, Margaret M. and Kofron, Ryan and Jensen, Camilla Nrgaard and McBurnett, Lauren R. and Snell, Marcus and Alderson, David L.", editor="Linkov, Igor and Palma-Oliveira, Jos{\'e} Manuel", title="Redesigning Resilient Infrastructure Research", booktitle="Resilience and Risk", year="2017", publisher="Springer Netherlands", address="Dordrecht", pages="81--119", abstract="Despite federal policy directives to strengthen the resilience of critical infrastructure systems to extreme weather and other adverse events, several knowledge and governance barriers currently frustrate progress towards policy goals, namely: (1) a lack of awareness of what constitutes resilience in diverse infrastructure applications, (2) a lack of judgement about how to create resilience, (3) a lack of incentives that motivate resilience creation, and (4) obstacles that prevent action or reform, even where incentives exist, within existing governance systems. In this chapter, we describe each of these barriers in greater detail and provide a catalog of theories for overcoming them. Regarding awareness, we contrast four different characterizations of resilience as rebound, robustness, graceful extensibility, and sustained adaptability. We apply Integral Theory to demonstrate the necessity of integrating multiple investigative perspectives. Further, we illustrate the importance of recognizing resilience as a set of processes, in addition to resources and outcomes, and the difficulty of measuring quality and quality of resilience actions. Regarding judgement, we position infrastructure as the principal mechanism by which human rights are realized as human capabilities, and propose applying theories of human development such as Maslow's hierarchy of needs to identify the most critical infrastructure in terms of the services they provide to end users. Regarding a lack of incentives, we examine the modes and tools of financial analysis by which investments in resilience infrastructure may be prioritized and find two failings: the difficulty of estimating the monetary value of optionality, and the problem of exponential discounting of future cash flows. Regarding obstacles to action, we describe a hierarchy of adaptive actions applicable to physical infrastructure and the essential dimensions of organizational maturity that determine how these adaptive actions might be initiated. Additionally, we discuss the difficulty of education and training for resilient infrastructure systems and propose simulation gaming as an integrative research and education approach for capturing lessons learned from historical catastrophes, play-testing scenarios, sharing knowledge, and training a workforce prepared for the challenges of the post-industrial infrastructure age. Finally, we suggest establishing a National Network for Resilient Infrastructure Simulation to coordinate research and practice focused on interactive case studies in resilient infrastructure systems.", isbn="978-94-024-1123-2" localurl="Documents/RedesigningResilientInfrastructureResearch_sm.pdf" }

Adaptive Capacity and Collaborative Governance

  1. @article{10.1093/jopart/mum032, author={Ansell, Chris and Gash, Alison}, title="{Collaborative Governance in Theory and Practice}", journal={Journal of Public Administration Research and Theory}, volume={18}, number={4}, pages={543-571}, year={2007}, month={11}, abstract="{Over the past few decades, a new form of governance has emerged to replace adversarial and managerial modes of policy making and implementation. Collaborative governance, as it has come to be known, brings public and private stakeholders together in collective forums with public agencies to engage in consensus-oriented decision making. In this article, we conduct a meta-analytical study of the existing literature on collaborative governance with the goal of elaborating a contingency model of collaborative governance. After reviewing 137 cases of collaborative governance across a range of policy sectors, we identify critical variables that will influence whether or not this mode of governance will produce successful collaboration. These variables include the prior history of conflict or cooperation, the incentives for stakeholders to participate, power and resources imbalances, leadership, and institutional design. We also identify a series of factors that are crucial within the collaborative process itself. These factors include face-to-face dialogue, trust building, and the development of commitment and shared understanding. We found that a virtuous cycle of collaboration tends to develop when collaborative forums focus on “small wins” that deepen trust, commitment, and shared understanding. The article concludes with a discussion of the implications of our contingency model for practitioners and for future research on collaborative governance.}", issn={1053-1858}, doi={10.1093/jopart/mum032}, url={https://doi.org/10.1093/jopart/mum032}, eprint={https://academic.oup.com/jpart/article-pdf/18/4/543/3808633/mum032.pdf}, localURL={Documents/Ansell_2008.pdf}, }
  2. @article{doi:10.1111/j.1541-0072.2010.00396.x, author={McGinnis, Michael D.}, title={Networks of Adjacent Action Situations in Polycentric Governance}, journal={Policy Studies Journal}, volume={39}, number={1}, pages={51-78}, keywords={institutional analysis, governance, fisheries, development, welfare, faith-based organizations}, doi={10.1111/j.1541-0072.2010.00396.x}, url={https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1541-0072.2010.00396.x}, eprint={https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1541-0072.2010.00396.x}, abstract={Within the Institutional Analysis and Development (IAD) framework, the concept of an action situation generalizes a game to allow for endogenous changes in its rules. This article re-visits this core concept to explore its potential for serving as the foundation for a systematic approach to the construction of more elaborate models of complex policy networks in which overlapping sets of actors have the ability to influence the rules under which their strategic interactions take place. Networks of adjacent action situations can be built on the basis of the seven distinct types of rules that define an action situation or by representing generic governance tasks identified in related research on local public economies. The potential of this extension of the IAD framework is demonstrated with simplified network representations of three diverse policy areas (Maine lobster fisheries, international development assistance, and the contribution of faith-based organizations to U.S. welfare policy).}, year={2011} localURL={Documents/McGinnis-2011-Policy_Studies_Journal.pdf}, }
  3. @article{article, author={Lubell, Mark}, year={2013}, month={08}, pages={}, title={Governing Institutional Complexity: The Ecology of Games Framework}, volume={41}, journal={Policy Studies Journal}, doi={10.1111/psj.12028} localURL={Documents/Lubell-2013-Policy_Studies_Journal.pdf}, }
  4. @article{doi:10.1146/annurev.energy.30.050504.144511, author={Folke, Carl and Hahn, Thomas and Olsson, Per and Norberg, Jon}, title={Adaptive Governance of Social-Ecological Systems}, journal={Annual Review of Environment and Resources}, volume={30}, number={1}, pages={441-473}, year={2005}, doi={10.1146/annurev.energy.30.050504.144511}, localURL={Documents/AdaptiveGovernanceOfSESs.pdf}, URL={https://doi.org/10.1146/annurev.energy.30.050504.144511}, eprint={https://doi.org/10.1146/annurev.energy.30.050504.144511}, abstract={We explore the social dimension that enables adaptive ecosystem-based management. The review concentrates on experiences of adaptive governance of social-ecological systems during periods of abrupt change (crisis) and investigates social sources of renewal and reorganization. Such governance connects individuals, organizations, agencies, and institutions at multiple organizational levels. Key persons provide leadership, trust, vision, meaning, and they help transform management organizations toward a learning environment. Adaptive governance systems often self-organize as social networks with teams and actor groups that draw on various knowledge systems and experiences for the development of a common understanding and policies. The emergence of “bridging organizations” seem to lower the costs of collaboration and conflict resolution, and enabling legislation and governmental policies can support self-organization while framing creativity for adaptive comanagement efforts. A resilient social-ecological system may make use of crisis as an opportunity to transform into a more desired state. } }
  5. @article{doi:10.1111/ssqu.12500, author={Zanocco, Chad M. and Jones, Michael D.}, title={Cultural Worldviews and Political Process Preferences*}, journal={Social Science Quarterly}, volume={99}, number={4}, pages={1377-1389}, doi={10.1111/ssqu.12500}, url={https://onlinelibrary.wiley.com/doi/abs/10.1111/ssqu.12500}, localURL={Documents/CulturalWorldviewsandPoliticalProcessPreferences.pdf}, eprint={https://onlinelibrary.wiley.com/doi/pdf/10.1111/ssqu.12500}, abstract={Objectives Cultural theory (CT) is often leveraged to explain policy preferences and risk perceptions. While scholars often make claims regarding CT's relationship with political process preferences, these remain largely untested. This study explores the relationship between CT and individual preferences toward the process in which political decisions are made. Methods Using national survey data (n=900), we identify two political process preference dimensions in exploratory factor analysis: compromise and expediency. To operationalize CT, survey items from cultural cognition theory are formed into cultural measures. We use bivariate and multivariate analysis to explore key relationships. Results Those with more egalitarian/communitarian worldviews value compromise in political decision making, while those with individualist/hierarchical worldviews are less likely to value compromise. We find no relationship between expediency and cultural worldviews. Conclusion This research suggests that CT is useful for understanding some, but not all, dimensions of political process preferences. While those with egalitarians/communitarian worldviews may be more accepting of policy decisions produced under compromise, other common tropes regarding the relationship between CT and process preferences should be carefully applied.}, year={2018} }
  6. @article{ABRAMS2019101977, title="The emergence of network governance in U.S. National Forest Administration: Causal factors and propositions for future research", journal="Forest Policy and Economics", volume="106", pages="101977", year="2019", issn="1389-9341", doi="https://doi.org/10.1016/j.forpol.2019.101977", url="http://www.sciencedirect.com/science/article/pii/S1389934119301376", localURL="Documents/EmergenceNetworkGovernanceUSNationalForestAdministration.pdf", author="Jesse Abrams", abstract="Since its establishment in the early twentieth century, the U.S. Forest Service has periodically evolved its approach to decision-making and management for the millions of hectares of national forest under its authority. Starting in the 1990s, a complex governance regime emerged in which non-Forest Service entities—such as state and other federal agencies, non-governmental organizations, public utilities, rural communities, and others—contribute resources and legitimacy to processes that include decision-making, project funding and implementation, monitoring, and changes to management rules and procedures. This review analyzes the origins of an emergent governance regime and provides a framework for analyzing contemporary patterns of national forest administration, structured around three key elements. Legitimacy is a necessary component of any continued public resource management regime, and in the current period this resource is (re)constructed through networks of governmental and non-governmental actors, with collaborative processes playing a central role. Capacity is needed to implement and evaluate resource management decisions, and the capacity of the Forest Service is frequently augmented through partnerships with non-federal entities. Institutional innovation is often needed to align Forest Service constitutional and operational rules with socially legitimate management actions, and this process may occur most often in situations characterized by the involvement of network actors. Five propositions are presented as contributions to a research agenda on national forest governance. This framework contributes to a better understanding of the causes and consequences of environmental governance changes affecting federal forest landscapes, key ecosystem processes, and the livelihoods of human communities throughout the U.S." }
  7. @inbook {63853, title={Institutional Rational Choice: An Assessment of the Institutional Analysis and Development Framework}, booktitle={Theories of the Policy Process}, series={Theoretical Lenses on Public Policy}, year={1999}, pages={35-71}, publisher={Westview Press}, organization={Westview Press}, address={Boulder, CO}, keywords={cipec, ifri, institutional analysis--IAD framework, public policy--models, public policy--theory, rational choice theory, rules--theory, Workshop}, isbn={0813399866}, author={Elinor Ostrom} }
  8. @article{10.1093/jopart/mum032, author={Ansell, Chris and Gash, Alison}, title={Collaborative Governance in Theory and Practice}, journal={Journal of Public Administration Research and Theory}, volume={18}, number={4}, pages={543-571}, year={2007}, month={11}, abstract={Over the past few decades, a new form of governance has emerged to replace adversarial and managerial modes of policy making and implementation. Collaborative governance, as it has come to be known, brings public and private stakeholders together in collective forums with public agencies to engage in consensus-oriented decision making. In this article, we conduct a meta-analytical study of the existing literature on collaborative governance with the goal of elaborating a contingency model of collaborative governance. After reviewing 137 cases of collaborative governance across a range of policy sectors, we identify critical variables that will influence whether or not this mode of governance will produce successful collaboration. These variables include the prior history of conflict or cooperation, the incentives for stakeholders to participate, power and resources imbalances, leadership, and institutional design. We also identify a series of factors that are crucial within the collaborative process itself. These factors include face-to-face dialogue, trust building, and the development of commitment and shared understanding. We found that a virtuous cycle of collaboration tends to develop when collaborative forums focus on “small wins” that deepen trust, commitment, and shared understanding. The article concludes with a discussion of the implications of our contingency model for practitioners and for future research on collaborative governance.}, issn={1053-1858}, doi={10.1093/jopart/mum032}, url={https://doi.org/10.1093/jopart/mum032}, localurl={/Documents/CollabGovernanceTheory.pdf}, eprint={http://oup.prod.sis.lan/jpart/article-pdf/18/4/543/3808633/mum032.pdf}, }
  9. @article {Bodineaan1114, author={Bodin, Orjan}, title={Collaborative environmental governance: Achieving collective action in social-ecological systems}, volume={357}, number={6352}, elocation-id={eaan1114}, year={2017}, doi={10.1126/science.aan1114}, publisher={American Association for the Advancement of Science}, abstract={By its nature, environmental governance requires collaboration. However, studies have shown that various types of stakeholders often lack the willingness to deliberate and contribute to jointly negotiated solutions to common environmental problems. Bodin reviews studies and cases that elucidate when, if, and how collaboration can be effective and what kind of environmental problems are most fruitfully addressed in this way. The piece provides general conclusions about the benefits and constraints of collaborative approaches to environmental management and governance and points out that there remain substantial knowledge gaps and key areas where more research is needed.Science, this issue.
    BACKGROUND
    Current and future generations are confronted with the complex task of devising sustainable solutions to environmental problems. The coming decade might determine whether humanity will be able to set a course toward a future of continued prosperity on a planet whose ecosystems will deliver the needed goods and services. A crucial piece of this puzzle is achieving effective collaboration among different public and private actors and stakeholders. Calls for solving environmental problems through collaborative governance emphasize benefits from local to global scales-from artisanal fishermen avoiding the overfishing of local fish stocks by together agreeing upon sustainable practices, to states jointly committing to implement adequate measures to reduce greenhouse gas emissions. Although commonly advocated, achieving successful collaborations when confronted with complex environmental problems spanning geographical scales and jurisdictional boundaries is an area where substantial knowledge gaps remain.
    ADVANCES
    A growing amount of empirical evidence shows the effectiveness of actors engaged in different collaborative governance arrangements in addressing environmental problems. However, studies also show that actors sometimes collaborate only as a means of advocating their own interests, while largely lacking a willingness to contribute towards jointly negotiated solutions to common problems. Hence, collaboration is sometimes unable to deliver any tangible outcomes, or merely produces symbolic outcomes such as aggregated wish lists where conflicts of interest are left untouched.Clearly, no single blueprint exists for how to succeed by using collaborative approaches to solve environmental problems. One way of approaching this puzzle is through the lenses of the participating actors and the ways in which they engage in collaboration with each other. This approach entails directing attention to who the actors are, what their interests and motives are, who they collaborate with, and how the structures of such collaborative networks relate to the actors joint abilities to address different environmental problems.Emerging insights from recent research suggest that the effectiveness of different collaborative network structures in addressing environmental problems depends on how those problems unfold with respect to the following characteristics: (i) varying levels of risk that actors free-ride on others efforts; (ii) varying levels of knowledge gaps, signifying different needs for social learning and deliberation among actors with different backgrounds, experiences, and interests; and (iii) whether these problems are, for all practical purposes, permanent or just temporary.Also, long-standing research questions regarding whether governance structures that are adequately aligned with ecosystem structures and processes are more effective have recently been addressed empirically. Early results suggest several ways in which misalignments between the structure of a collaborative network and the biophysical environment reduce the ability to address environmental problems effectively.
    OUTLOOK
    A more nuanced understanding of whether collaborative governance is the most effective way of solving environmental problems is needed. The capacity of collaborative governance to deliver sustainable solutions for any given environmental problem ranges from highly effective to essentially worthless. Future efforts must establish which factors determine the exact location of any collaborative arrangement on this continuum.Emerging insights suggest that where a collaborative arrangement falls on the spectrum results from a complex interplay between several factors. The characteristics of the underlying collective action problem are one factor. Others are the characteristics of the underlying biophysical system and how these align with the ways in which collaborative governance arrangements are constructed, institutionally embedded, and managed. Finally, the patterns in which actors collaborate with each other (or do not) is a factor that potentially determines the effects that the other factors have on a collaborative arrangement's ability to solve environmental problems.Small-scale fishermen preparing their nets.Although collaborative approaches to environmental governance are increasingly advocated, a better understanding of if and how multiactor collaboration in interlinked social-ecological systems is able to effectively address various environmental problems is urgently needed.Photo: Nature Picture Library/Alamy~Stock PhotoManaging ecosystems is challenging because of the high number of stakeholders, the permeability of man-made political and jurisdictional demarcations in relation to the temporal and spatial extent of biophysical processes, and a limited understanding of complex ecosystem and societal dynamics. Given these conditions, collaborative governance is commonly put forward as the preferred means of addressing environmental problems. Under this paradigm, a deeper understanding of if, when, and how collaboration is effective, and when other means of addressing environmental problems are better suited, is needed. Interdisciplinary research on collaborative networks demonstrates that which actors get involved, with whom they collaborate, and in what ways they are tied to the structures of the ecosystems have profound implications on actors' abilities to address different types of environmental problems.}, issn={0036-8075}, URL={https://science.sciencemag.org/content/357/6352/eaan1114}, localurl={/Documents/CollabEnvGovernanceCollectiveAction.pdf}, eprint={https://science.sciencemag.org/content/357/6352/eaan1114.full.pdf}, journal={Science} }
  10. @article{, title="Capacity to adapt to environmental change: evidence from a network of organizations concerned with increasing wildfire risk", journal="Ecology and Society", volume="22", number="1", year="2017", localURL="Documents/CapAdaptToEnvChangeWildfire.pdf", author="Fischer, A. Paige and Jasny, Lorien", abstract="Because wildfire size and frequency are expected to increase in many forested areas in the United States, organizations involved in forest and wildfire management could arguably benefit from working together and sharing information to develop strategies for how to adapt to this increasing risk. Social capital theory suggests that actors in cohesive networks are positioned to build trust and mutual understanding of problems and act collectively to address these problems, and that actors engaged with diverse partners are positioned to access new information and resources that are important for innovation and complex problem solving. We investigated the patterns of interaction within a network of organizations involved in forest and wildfire management in Oregon, USA, for evidence of structural conditions that create opportunities for collective action and learning. We used descriptive statistical analysis of social network data gathered through interviews to characterize the structure of the network and exponential random graph modeling to identify key factors in the formation of network ties. We interpreted our findings through the lens of social capital theory to identify implications for the network’s capacity to engage in collective action and complex problem-solving about how to adapt to environmental change. We found that tendencies to associate with others with similar management goals, geographic emphases, and attitudes toward wildfire were strong mechanisms shaping network structure, potentially constraining interactions among organizations with diverse information and resources and limiting opportunities for learning and complex problem-solving needed for adaptation. In particular, we found that organizations with fire protection and forest restoration goals comprised distinct networks despite sharing concern about the problem of increasing wildfire risk." }
  11. @article{FISCHER201618, title="A network approach to assessing social capacity for landscape planning: The case of fire-prone forests in Oregon, USA", journal="Landscape and Urban Planning", volume="147", pages="18-27", year="2016", issn="0169-2046", doi="https://doi.org/10.1016/j.landurbplan.2015.10.006", url="http://www.sciencedirect.com/science/article/pii/S0169204615002170", localURL="Documents/NetApproachAssessingSocialCapacityFireProneLandscapes.pdf", author="A. Fischer and Ken Vance-Borland and Lorien Jasny and Kerry E. Grimm and Susan Charnley", keywords="Organizational networks, Social network analysis, Social capital, Landscape planning, Wildfire management, Forest restoration", abstract="Management of ecological conditions and processes in multiownership landscapes requires cooperation by diverse stakeholder groups. The structure of organizational networks – the extent to which networks allow for interaction among organizations within and across ideological and geographic boundaries – can indicate potential opportunities for cooperation on landscape-scale problems. In the arid landscapes of the western United States, where increasingly large wildfires burn irrespective of property boundaries and land designations, organizations involved in the restoration of forests and the protection of property from wildfire could benefit from working together to share information and coordinate strategies. We investigated patterns of interaction among organizations concerned with increasingly uncharacteristic wildfire risk in the Eastern Cascades Ecoregion of Oregon for evidence of structural conditions that create opportunity for cooperation. Through social network analysis of interview data, we found that despite sharing concern about wildfire risk in an area with a common set of ecological conditions, organizations with forest restoration and fire protection goals comprised distinct networks, as did organizations that focused on different geographic areas of the ecoregion. When interpreted through the lens of social capital and organizational theory these findings raise questions about the extent to which the structure of the organizational network reflects capacity to address wildfire risk in fire-prone forests on the ecoregion-scale. This study provides insights on the utility of a structural approach for investigating social capacity for landscape-scale planning." }
  12. @article{SAYLES201764, title="Who collaborates and why: Assessment and diagnostic of governance network integration for salmon restoration in Puget Sound, USA", journal="Journal of Environmental Management", volume="186", pages="64-78", year="2017", issn="0301-4797", doi="https://doi.org/10.1016/j.jenvman.2016.09.085", url="http://www.sciencedirect.com/science/article/pii/S0301479716307563", localURL="Documents/WhoCollaboratesAndWhy.pdf", author="Jesse S. Sayles and Jacopo A. Baggio", keywords="Scale mismatch, Social-ecological fit, Social network analysis, Environmental governance, Watershed restoration, Collaborative management", abstract="Governance silos are settings in which different organizations work in isolation and avoid sharing information and strategies. Siloes are a fundamental challenge for environmental planning and problem solving, which generally requires collaboration. Siloes can be overcome by creating governance networks. Studying the structure and function of these networks is important for understanding how to create institutional arrangements that can respond to the biophysical dynamics of a specific natural resource system (i.e., social-ecological, or institutional fit). Using the case of salmon restoration in a sub-basin of Puget Sound, USA, we assess network integration, considering three different reasons for network collaborations (i.e., mandated, funded, and shared interest relationships) and analyze how these different collaboration types relate to productivity based on practitioner's assessments. We also illustrate how specific and targeted network interventions might enhance the network. To do so, we use a mixed methods approach that combines quantitative social network analysis (SNA) and qualitative interview analysis. Overall, the sub-basin's governance network is fairly well integrated, but several concerning gaps exist. Funded, mandated, and shared interest relationships lead to different network patterns. Mandated relationships are associated with lower productivity than shared interest relationships, highlighting the benefit of genuine collaboration in collaborative watershed governance. Lastly, quantitative and qualitative data comparisons strengthen recent calls to incorporate geographic space and the role of individual actors versus organizational culture into natural resource governance research using SNA." }
  13. @article{HAMILTON2019113, title="A social-ecological network approach for understanding wildfire risk governance", journal="Global Environmental Change", volume="54", pages="113-123", year="2019", issn="0959-3780", doi="https://doi.org/10.1016/j.gloenvcha.2018.11.007", url="http://www.sciencedirect.com/science/article/pii/S0959378017312232", localURL="Documents/SNApproachforWildfireRiskGovernance.pdf", author="Matthew Hamilton and Alexandra Paige Fischer and Alan Ager", keywords="Risk interdependence, Social-ecological networks, Exponential random graph models, Wildfire", abstract="Large wildfire events (e.g. >100 square km) highlight the importance of governance systems that address wildfire risk at landscape scales and among multiple land owners and institutions. A growing body of empirical work demonstrates that environmental governance outcomes depend upon how well patterns of interaction among actors align with patterns of ecological connectivity, such as wildfire transmission. However, the factors that facilitate or inhibit this alignment remain poorly understood. It is crucial to improve understanding of the conditions under which actors establish or maintain linkages with other actors with whom they are interdependent because of ecological linkages. To this end, we introduce the concept of “risk interdependence archetypes” based on the spatial configurations by which one actor (i.e. a particular organization) is exposed to risk via the actions of another actor. We then develop a set of hypotheses to explore how different sets of conditions associated with each spatial configurations of risk interdependence may shape the likelihood that an actor coordinates with another actor in ways that promote social-ecological alignment. We test these hypotheses using network analysis of a wildfire transmission network developed through simulation of wildfires over several thousand fire seasons and a governance network created from interviews with 154 representatives of 87 organizations involved in efforts to mitigate wildfire risk in the Eastern Cascades Ecoregion, USA. Results indicate that social-ecological alignment is more likely when actors have opportunities to influence forest management practices on ignition-prone lands that they do not manage themselves, and when actors bear greater responsibility for averting losses from wildfires that spread to lands they manage independently. Importantly, not all forms of risk interdependence increase the likelihood of alignment, implying that organizations have limited capacity for interaction and may prioritize certain risk mitigation partnerships over others. While the performance of risk governance systems may hinge on the alignment of social and ecological networks, our results suggest that alignment in turn may depend on actor-level strategies for interaction with other actors." }
  14. @article{BERGSTEN201927, title="Identifying governance gaps among interlinked sustainability challenges", journal="Environmental Science & Policy", volume="91", pages="27 - 38", year="2019", issn="1462-9011", doi="https://doi.org/10.1016/j.envsci.2018.10.007", url="http://www.sciencedirect.com/science/article/pii/S1462901118303010", localURL="Documents/IdentifyingGovernanceGaps.pdf", author="Arvid Bergsten and Tolera Senbeto Jiren and Julia Leventon and Ine Dorresteijn and Jannik Schultner and Joern Fischer", keywords="Collaborative governance, Integrative management, Interdependent problems, Sustainable development, Institutional fit, Network", abstract="Sustainability issues cannot be separated from their social and biophysical context, and collaborative governance responses to interdependent sustainability issues are inherently complex. Governance gaps emerge when responsible actors fail to recognize how multiple issues and actors are interlinked. Closing governance gaps is particularly challenging for sustainability issues that intersect several sectors of society, such as livelihoods, agriculture and biodiversity conservation. This study introduces a new quantitative empirical approach that conceptualizes how governance gaps emerge at the intersection of two networks that are usually studied separately: an actor network and a network of interdependent sustainability issues. We differentiate between (1) integrative gaps that arise when interdependent issues are managed in separation without recognizing their interdependencies, versus (2) collaborative gaps that arise when actors working on common issues do not collaborate. Using data on 60 actors and 38 sustainability issues in southwest Ethiopia, we found comprehensive collaboration networks around, for example, agricultural production and land-use issues, but large collaborative gaps for forest and wildlife issues. While actors actively managed interdependencies around national high-priority issues such as coffee export and family planning, integrative gaps were common for low-profile issues such as access provision of finance, transportation, schools, food and crop markets. In general, smaller specialized actors had a stronger tendency than larger generalist actors to focus their management capacity towards the closing of governance gaps. Surprisingly, greater system complexity did not per se cause governance gaps, except when system interactions were cross-sectoral. Furthermore, our data suggested that integrative system management and collaboration reinforced each other. In conclusion, our network framework advances how governance gaps can be understood and prioritized in different empirical contexts. It enables a theoretically informed empirical identification of the specific sustainability issues for which targeted structural changes are most likely to facilitate improved sustainability outcomes." }

Community Vulnerability and Resilience

  1. @article{10.1371/journal.pone.0205825}, author={Davies, Ian P. and Haugo, Ryan D. and Robertson, James C. and Levin, Phillip S.}, journal={PLOS ONE}, publisher={Public Library of Science}, title={The unequal vulnerability of communities of color to wildfire}, year={2018}, month={11}, volume={13}, url={https://doi.org/10.1371/journal.pone.0205825}, localurl={Documents/Davies-2018-The-unequal-vulnerability-of-commun.pdf}, pages={1-15}, abstract={Globally, environmental disasters impact billions of people and cost trillions of dollars in damage, and their impacts are often felt most acutely by minority and poor communities. Wildfires in the U.S. have similarly outsized impacts on vulnerable communities, though the ethnic and geographic distribution of those communities may be different than for other hazards. Here, we develop a social-ecological approach for characterizing fire vulnerability and apply it to >70,000 census tracts across the United States. Our approach incorporates both the wildfire potential of a landscape and socioeconomic attributes of overlying communities. We find that over 29 million Americans live with significant potential for extreme wildfires, a majority of whom are white and socioeconomically secure. Within this segment, however, are 12 million socially vulnerable Americans for whom a wildfire event could be devastating. Additionally, wildfire vulnerability is spread unequally across race and ethnicity, with census tracts that were majority Black, Hispanic or Native American experiencing ca. 50% greater vulnerability to wildfire compared to other census tracts. Embracing a social-ecological perspective of fire-prone landscapes allows for the identification of areas that are poorly equipped to respond to wildfires.}, number={11}, doi={10.1371/journal.pone.0205825}
  2. @article{FlanaganGregoryHalliseyHeitgerdLewis+2011} author={Barry E. Flanagan and Edward W. Gregory and Elaine J Hallisey and Janet L. Heitgerd and Brian Lewis}, doi={doi:10.2202/1547-7355.1792}, url={https://doi.org/10.2202/1547-7355.1792}, localurl={Documents/10.2202_1547-7355.1792.pdf}, title={A Social Vulnerability Index for Disaster Management}, journal={Journal of Homeland Security and Emergency Management}, number={1}, volume={8}, year={2011}
  3. @article{doi:10.1061/(ASCE)NH.1527-6996.0000027}, author={Ryan Ojerio and Cassandra Moseley and Kathy Lynn and Neil Bania }, title={Limited Involvement of Socially Vulnerable Populations in Federal Programs to Mitigate Wildfire Risk in Arizona}, journal={Natural Hazards Review}, volume={12}, number={1}, pages={28-36}, year={2011}, doi={10.1061/(ASCE)NH.1527-6996.0000027} URL={https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29NH.1527-6996.0000027}, localurl={Documents/Ojerio et al 2011 Limited involvement of socially vulnerable populations in federal programs to mitigate wildfire risk in Arizona.pdf}, eprint={https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29NH.1527-6996.0000027}, abstract={ Currently, biophysical risk factors figure prominently in federal resource allocation to communities threatened by wildfire. Yet, disaster research demonstrates that socioeconomic characteristics impact disaster risk and resilience. Consequently, this study evaluates whether federal wildfire program resources are reaching socially vulnerable populations. Biophysical and social vulnerability indicators were included in a series of regressions to identify predictors of participation in three wildfire mitigation programs in Arizona. Findings indicate that mitigation activities are closely correlated with biophysical risk to wildfire, but socially vulnerable communities are less likely to participate even when exposed to high wildfire risk. This finding suggests a need for a more strategic and equitable distribution of federal resources to mitigate wildfire risk. }

Cultural Theory

  1. @article{doi:10.1002/rhc3.12183, author={Kyne, Dean and Aldrich, Daniel P.}, title={Capturing Bonding, Bridging, and Linking Social Capital through Publicly Available Data}, journal={Risk, Hazards & Crisis in Public Policy}, volume={}, number={}, pages={}, keywords={bonding social ties, bridging social capital, linking social capital, disaster management}, doi={10.1002/rhc3.12183}, localurl={Documents/Kyne_Aldrich_2019.pdf}, url={https://onlinelibrary.wiley.com/doi/abs/10.1002/rhc3.12183}, eprint={https://onlinelibrary.wiley.com/doi/pdf/10.1002/rhc3.12183}, abstract={A growing body of research has illuminated the powerful role played by social capital in influencing disaster and resilience outcomes. Popular vulnerability mapping frameworks, while well suited for capturing demographic characteristics such as age, race, and wealth, do not include sufficient proxies for social capital. This article proposes a concrete way to measure bonding, bridging, and linking social capital using widely available information. Our social capital index (SoCI) uses 19 indicators from publicly available U.S. census and Environmental Systems Research Institute (ESRI) data for all counties across the contiguous United States. We demonstrate broad variations in the SoCI Index by mapping counties across the continental North America. Validity tests indicate outcomes similar or superior to other approaches such as the Baseline Resilience Indicators for Communities (BRIC) and the Social Vulnerability Index (SoVI). Our new mapping framework provides a more focused way for disaster managers, scholars, and local residents to understand how communities could cope with future disasters based on levels of social ties and cohesion.} }
  2. @article{article, author={Johnson, Branden and Swedlow, Brendon}, year={2019}, month={01}, pages={}, localurl={Documents/Johnson_Swedlow_2019.pdf}, title={Comparing Cultural Theory and Cultural Cognition Theory Survey Measures to Each Other and as Explanations for Judged Risk}, journal={SSRN Electronic Journal}, doi={10.2139/ssrn.3345257} }
  3. @article{doi:10.1111/psj.12077, author={Weare, Christopher and Lichterman, Paul and Esparza, Nicole}, title={Collaboration and Culture: Organizational Culture and the Dynamics of Collaborative Policy Networks}, journal={Policy Studies Journal}, volume={42}, number={4}, pages={590-619}, keywords={interorganizational collaboration, policy networks, social networks, culture, cultural theory, housing policy}, doi={10.1111/psj.12077}, url={https://onlinelibrary.wiley.com/doi/abs/10.1111/psj.12077}, localurl={Documents/weare_et.al_2014.pdf}, eprint={https://onlinelibrary.wiley.com/doi/pdf/10.1111/psj.12077}, abstract={This paper presents a theory of the role of culture in collaborative policy networks. It builds on the literature that analyzes the factors related to the formation, maintenance, and dissolution of collaborative arrangements by demonstrating the importance of hitherto undertheorized cultural factors. Cultural theory indicates that actors with different cultural viewpoints have distinct and predictable biases in terms of their expectations of collaboration and their preferences concerning how collaborative policy networks are structured. These biases, in turn, shape how collaborative partners are chosen and how collaborative relationships are maintained over time. The theory is illustrated with a case study of the rise and dissolution of a coalition within a housing policy network in Los Angeles. The case illustrates that cultural differences can impede collaboration even when organizations share similar policy goals.}, year={2014} }
  4. @article{article, author={Jones, Michael}, year={2011}, month={10}, pages={720 - 725}, title={Leading the Way to Compromise? Cultural Theory and Climate Change Opinion}, volume={44}, localURL={Documents/CulturalTheory.pdf} journal={PS: Political Science & Politics}, doi={10.1017/S104909651100134X} abstract= {Climate change is easily one of the most contentious policy problems facing the United States. A majority of climate scientists agree that the earth has warmed over the last 100 years and that human-made greenhouse gasses are the cause (e.g., Doran and Zimmerman 2009; IPCC 2007; Oreskes 2004, but also see Bray 2010), yet a nontrivial portion of the US population diverges sharply from this dominant scientific position (see, for example, Jenkins-Smith, Herron, and Silva 2010, 41-45; Leiserowitz 2006; Nisbet and Myers 2007). Why? Past research usually points to the public's lack of climate change knowledge (e.g., Kellstedt, Zahran, and Vedlitz 2008), finds that media over report the views of climate change skeptics in a misplaced quest for "balanced" reporting (e.g., Boykoff and Boykoff 2007, but see Swedlow and Wildavsky 1995), or the public simply take cues from opinion leaders whom they trust (e.g., Malka, Krosnick, and Langer 2009). This article moves beyond the predominant concern with climate change knowledge, messaging structures, and cue taking in past research, and shifts the focus to characteristics intrinsic to the individual. The research presented here assesses the extent that the cultural theory (CT) developed by Mary Douglas, Aaron Wildavsky, and others (see, e.g., Schwarz and Thompson 1990; Thompson, Ellis, and Wildavsky 1990) can help political scientists understand why so many Americans do not align themselves with the majority of scientists and can help policy makers broker compromises on climate change policy.}

Social Network Modeling

  1. @article{Wang_van Voorn_Grant_Zare_Giupponi_Steinmann_Müller_Elsawah_van Delden_Athanasiadis_Sun_Jager_Little_Jakeman_2023, title={Scale decisions and good practices in socio-environmental systems modelling: guidance and documentation during problem scoping and model formulation}, volume={5}, url={https://sesmo.org/article/view/18563}, doi={10.18174/sesmo.18563}, abstract={Models of socio-environmental or social-ecological systems (SES) commonly address problems requiring interdisciplinary scientific expertise and input from a heterogeneous group of stakeholders. In SES modelling multiple interactions occur on different scales among various phenomena. These scale phenomena include the technical, such as system variables, process detail, inputs and outputs, which most often require spatial, temporal, thematic and organisational choices. From a good practice and project efficiency perspective, the problem scoping and conceptual model formulation phase of modelling is the one to address well from the outset. During this phase, intense and substantive discussions should arise regarding appropriate scales at which to represent the different phenomena. Although the details of these discussions influence the path of model development, they are seldom documented and as a result often forgotten. We draw upon personal experience with existing protocols and communications in recent literature to propose preliminary guidelines for documenting these early discussions about the scale(s) of the studied phenomena. Our guidelines aim to aid modelling group members in building and capturing the richness of their rationale for scoping and scale decisions. The resulting transcripts are intended to promote transparency of modelling decisions and provide essential support for the justification of the final model for its intended use. They also facilitate adaptive modifications of the pathway of model development via retracing decisions and iterative reflection upon alternative scale options.}, journal={Socio-Environmental Systems Modelling}, author={Wang, Hsiao-Hsuan and van Voorn, George and Grant, William E. and Zare, Fateme and Giupponi, Carlo and Steinmann, Patrick and Müller, Birgit and Elsawah, Sondoss and van Delden, Hedwig and Athanasiadis, Ioannis N. and Sun, Zhanli and Jager, Wander and Little, John C. and Jakeman, Anthony J.}, year={2023}, month={Mar.}, localurl={Documents/Wang-2023-Scale-decisions-and-good-practices-.pdf}, }
  2. @article {Aral21544, author={Aral, Sinan and Muchnik, Lev and Sundararajan, Arun}, title={Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks}, volume={106}, number={51}, pages={21544--21549}, year={2009}, doi={10.1073/pnas.0908800106}, publisher={National Academy of Sciences}, abstract={Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users{\textquoteright} longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300{\textendash}700\%, and that homophily explains \>50\% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.}, issn={0027-8424}, URL={https://www.pnas.org/content/106/51/21544}, eprint={https://www.pnas.org/content/106/51/21544.full.pdf}, journal={Proceedings of the National Academy of Sciences}, localurl={Documents/21544.full.pdf}, }
  3. @article {Romero1, author={Daniel Romero and Brendan Meeder and Jon Michael Kleinberg}, title={Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter}, pages={695–704}, year={2011}, doi={https://doi.org/10.1145/1963405.1963503}, doi={https://doi.org/10.1145/1963405.1963503}, abstract={There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widely-used hashtags on different topics spread. Our results show that this variation is not attributable simply to differences in "stickiness," the probability of adoption based on one or more exposures, but also to a quantity that could be viewed as a kind of "persistence" - the relative extent to which repeated exposures to a hashtag continue to have significant marginal effects. We find that hashtags on politically controversial topics are particularly persistent, with repeated exposures continuing to have unusually large marginal effects on adoption; this provides, to our knowledge, the first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious. Among other findings, we discover that hashtags representing the natural analogues of Twitter idioms and neologisms are particularly non-persistent, with the effect of multiple exposures decaying rapidly relative to the first exposure. We also study the subgraph structure of the initial adopters for different widely-adopted hashtags, again finding structural differences across topics. We develop simulation-based and generative models to analyze how the adoption dynamics interact with the network structure of the early adopters on which a hashtag spreads.}, URL={https://dl.acm.org/doi/abs/10.1145/1963405.1963503}, journal={WWW 11: Proceedings of the 20th international conference on World wide web}, localurl={Documents/1963405.1963503.pdf}, }
  4. @article {Centola1194, author={Centola, Damon}, title={The Spread of Behavior in an Online Social Network Experiment}, volume={329}, number={5996}, pages={1194--1197}, year={2010}, doi={10.1126/science.1185231}, publisher={American Association for the Advancement of Science}, abstract={How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.}, issn={0036-8075}, URL={https://science.sciencemag.org/content/329/5996/1194}, eprint={https://science.sciencemag.org/content/329/5996/1194.full.pdf}, journal={Science}, localurl={Documents/1194.full.pdf}, }
  5. @article{ author={Kuikka, Vesa}, year={2018}, title={Influence spreading model used to analyse social networks and detect sub-communities}, journal={Computational Social Networks}, abstract={A dynamic influence spreading model is presented for computing network centrality and betweenness measures. Network topology, and possible directed connections and unequal weights of nodes and links, are essential features of the model. The same influence spreading model is used for community detection in social networks and for analysis of network structures. Weaker connections give rise to more sub-communities whereas stronger ties increase the cohesion of a community. The validity of the method is demonstrated with different social networks. Our model takes into account different paths between nodes in the network structure. The dependency of different paths having common links at the beginning of their paths makes the model more realistic compared to classical structural, simulation and random walk models. The influence of all nodes in a network has not been satisfactorily understood. Existing models may underestimate the spreading power of interconnected peripheral nodes as initiators of dynamic processes in social, biological and technical networks.}, url={https://doi.org/10.1186/s40649-018-0060-z}, localURL={Documents/s40649-018-0060-z.pdf} }
  6. @INPROCEEDINGS{7463733, author={Y. Li and X. Wu and L. Li}, booktitle={2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)}, title={Community Influence Analysis Based on Social Network Structures}, year={2015}, pages={247-254}, abstract={Modeling and measuring social influence is a major problem in Social Network Analysis. Existing models and methods could handle individual influence analysis conveniently, but they rarely estimate the social influence of communities which are ubiquitous in social networks. Based on the structures of online social networks, a community oriented influence analysis model is proposed. Then, we provide an algorithm called CommRank for calculating the social influence of communities. Since the algorithm combines both internal structural information and external interaction data of communities, it estimates community influence more precisely on multiple datasets. Experimental results also demonstrate that, at the cost of a little gain loss, CommRank can dramatically improve the efficiency when dealing with the influence maximization problem.}, keywords={optimisation;social networking (online);social sciences computing;influence maximization problem;CommRank;community oriented influence analysis model;online social network;social network structure;Social network services;Algorithm design and analysis;Approximation algorithms;Tuning;Damping;Computer science;Analytical models;social network;community influence;CommRank algorithm;influence maximization problem}, doi={10.1109/SmartCity.2015.79}, url={https://ieeexplore.ieee.org/document/7463733}, localURL={Document/07463733.pdf}
  7. @article{ICWSM101530, author={Sun J. and Tang J}, title={A Survey of Models and Algorithms for Social Influence Analysis}, year={2011}, keywords={Social Network; Social Influence}, abstract={Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this chapter, we survey the research on social influence analysis with a focus on the computational aspects. First, we present statistical measurements related to social influence. Second, we describe the literature on social similarity and influences. Third, we present the research on social influence maximization which has many practical applications including marketing and advertisement.}, localURL={Documents/social-influence-analysis.pdf}, url={https://doi.org/10.1007/978-1-4419-8462-3_7} }
  8. @article{ICWSM101530, author={Dan Cosley and Daniel Huttenlocher and Jon Kleinberg and Xiangyang Lan and Siddharth Suri}, title={Sequential Influence Models in Social Networks}, conference={International AAAI Conference on Web and Social Media}, year={2010}, keywords={Social Network; Social Influence}, abstract={The spread of influence among individuals in a social network can be naturally modeled in a probabilistic framework, but it is challenging to reason about differences between various models as well as to relate these models to actual social network data. Here we consider two of the most fundamental definitions of influence, one based on a small set of "snapshot'' observations of a social network and the other based on detailed temporal dynamics. The former is particularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter however provides a more detailed process model of how influence spreads. We study the relationship between these two ways of measuring influence, in particular establishing how to infer the more detailed temporal measure from the more readily observable snapshot measure. We validate our analysis using the history of social interactions on Wikipedia; the result is the first large-scale study to exhibit a direct relationship between snapshot and temporal models of social influence.}, localURL={Documents/cosley10sequential.pdf}, url={https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1530} }
  9. @article{flache2017, title={Models of Social Influence: Towards the Next Frontiers}, author={Flache, Andreas and M\"{a}s, Michael and Feliciani, Thomas and Chattoe-Brown, Edmund and Deffuant, Guillaume and Huet, Sylvie and Lorenz, Jan}, journal={Journal of Artificial Societies and Social Simulation}, ISSN={1460-7425}, volume={20}, number={4}, pages={2}, year={2017}, URL={http://jasss.soc.surrey.ac.uk/20/4/2.html}, localURL={Documents/2.pdf}, DOI={10.18564/jasss.3521}, keywords={Social Influence, Opinion Dynamics, Polarization, Calibration and Validation, Micro-Macro Link}, abstract={In 1997, Robert Axelrod wondered in a highly influential paper "If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear?" Axelrod’s question highlighted an ongoing quest for formal theoretical answers joined by researchers from a wide range of disciplines. Numerous models have been developed to understand why and under what conditions diversity in beliefs, attitudes and behavior can co-exist with the fact that very often in interactions, social influence reduces differences between people. Reviewing three prominent approaches, we discuss the theoretical ingredients that researchers added to classic models of social influence as well as their implications. Then, we propose two main frontiers for future research. First, there is urgent need for more theoretical work comparing, relating and integrating alternative models. Second, the field suffers from a strong imbalance between a proliferation of theoretical studies and a dearth of empirical work. More empirical work is needed testing and underpinning micro-level assumptions about social influence as well as macro-level predictions. In conclusion, we discuss major roadblocks that need to be overcome to achieve progress on each frontier. We also propose that a new generation of empirically-based computational social influence models can make unique contributions for understanding key societal challenges, like the possible effects of social media on societal polarization.}, }
  10. @article{LI201840, title={Social Influence Analysis: Models, Methods, and Evaluation}, journal={Engineering}, volume={4}, number={1}, pages={40-46}, year={2018}, note={Cybersecurity}, issn={2095-8099}, doi={https://doi.org/10.1016/j.eng.2018.02.004}, url={http://www.sciencedirect.com/science/article/pii/S2095809917308056}, localURL={Documents/1-s2.0-S2095809917308056-main.pdf}, author={Kan Li and Lin Zhang and Heyan Huang}, keywords={Social influence analysis, Online social networks, Social influence analysis models, Influence evaluation}, abstract={Social influence analysis (SIA) is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and evaluation aspects related to SIA. We divide SIA models into two types: microscopic and macroscopic models. Microscopic models consider human interactions and the structure of the influence process, whereas macroscopic models consider the same transmission probability and identical influential power for all users. We analyze social influence methods including influence maximization, influence minimization, flow of influence, and individual influence. In social influence evaluation, influence evaluation metrics are introduced and social influence evaluation models are then analyzed. The objectives of this paper are to provide a comprehensive analysis, aid in understanding social behaviors, provide a theoretical basis for influencing public opinion, and unveil future research directions and potential applications.} }
  11. @article {Contractor13650, author={Contractor, Noshir S. and DeChurch, Leslie A.}, title={Integrating social networks and human social motives to achieve social influence at scale}, volume={111}, number={Supplement 4}, pages={13650--13657}, year={2014}, doi={10.1073/pnas.1401211111}, publisher={National Academy of Sciences}, abstract={The innovations of science often point to ideas and behaviors that must spread and take root in communities to have impact. Ideas, practices, and behaviors need to go from accepted truths on the part of a few scientists to commonplace beliefs and norms in the minds of the many. Moving from scientific discoveries to public good requires social influence. We introduce a structured influence process (SIP) framework to explain how social networks (i.e., the structure of social influence) and human social motives (i.e., the process of social influence wherein one person{\textquoteright}s attitudes and behaviors affect another{\textquoteright}s) are used collectively to enact social influence within a community. The SIP framework advances the science of scientific communication by positing social influence events that consider both the {\textquotedblleft}who{\textquotedblright} and the {\textquotedblleft}how{\textquotedblright} of social influence. This framework synthesizes core ideas from two bodies of research on social influence. The first is network research on social influence structures, which identifies who are the opinion leaders and who among their network of peers shapes their attitudes and behaviors. The second is research on social influence processes in psychology, which explores how human social motives such as the need for accuracy or the need for affiliation stimulate behavior change. We illustrate the practical implications of the SIP framework by applying it to the case of reducing neonatal mortality in India.}, issn={0027-8424}, URL={https://www.pnas.org/content/111/Supplement_4/13650}, localURL={Documents/13650.pdf}, eprint={https://www.pnas.org/content/111/Supplement_4/13650.full.pdf}, journal={Proceedings of the National Academy of Sciences} }
  12. @article{article, author={Robins, Garry and Pattison, Philippa and Elliott, Peter}, year={2001}, month={02}, pages={161-189}, title={Network models for social influence processes}, volume={66}, journal={Psychometrika}, abstract={This paper generalizes the p∗ class of models for social network data to predict individual-level attributes from network ties. The p∗ model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so construct p∗ models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees’ reactions to aspects of the course found to relate to those of their network partners.} localURL={Documents/Network_models_for_social_influence_processes.pdf}, URL={https://www.researchgate.net/publication/24063372} doi={10.1007/BF02294834} }
  13. @article{NAP10735, author={Martina Morris}, editor={Ronald Breiger and Kathleen Carley and Philippa Pattison}, title={Local Rules and Global Properties: Modeling the Emergence of Network Structure}, isbn={978-0-309-08952-4}, doi={10.17226/10735}, abstract={This paper reviews the interaction between theory and methods in the field of network analysis. Network theory has traditionally sought to use social relations to bridge what social scientists refer to as the micro-macro gap: understanding how social structures are formed by the accumulation of simple rules operating on local relations. While early network methods reflected this goal, the bulk of the methods developed later and popularized in computer packages were more descriptive and static. That is beginning to change again with recent developments in statistical methods for network analysis. One particularly promising approach is based on exponential random graph models (ERGM). ERGM were first applied in the context of spatial statistics, and they provide a general framework for modeling dependent data where the dependence can be thought of as a neighborhood effect. The models can be used to decompose overall network structural properties into the effects of localized interaction rules; the traditional concern of the field. An example is given using an HIV transmission network. }, url={https://www.nap.edu/catalog/10735/dynamic-social-network-modeling-and-analysis-workshop-summary-and-papers}, localURL={Documents/174-186.pdf}, year={2003}, publisher={The National Academies Press}, address={Washington, DC} }
  14. @article{NAP10736, author={Noah E. Friedkin}, editor={Ronald Breiger and Kathleen Carley and Philippa Pattison}, title={Social Influence Network Theory - Towards as Science of Strategic Modification of Interpersonal Influence Systems}, isbn={978-0-309-08952-4}, doi={10.17226/10735}, abstract={Social influence network theory is a mathematical formalization of the process of interpersonal influencethat occurs in groups, affects persons’ attitudes and opinions on issues, and produces interpersonalagreements, including group consensus, from an initial state of disagreement. The theory also may beemployed to predict the consequences of particular modifications of a social influence system. Adescription of social influence network theory is presented. Using network data from a field study of apolicy group, simulated modifications of an influence system and the consequences of thesemodifications are described. The illustration introduces a large subject: the development of a scientificbasis for constructing and modifying the social structures of groups so that the expected outcomes of theinfluence system of a group will be close to desirable optimal outcomes for the group with some pre-specified degree of reliability. Toward the development of such a science and within the framework ofsocial influence network theory, some key lines of research are outlined that are related to the operationand structural dynamics of interpersonal influence networks and that, in my view, would advance thedevelopment of a science concerned with the strategic modification of interpersonal influence systems.}, url={https://www.nap.edu/catalog/10735/dynamic-social-network-modeling-and-analysis-workshop-summary-and-papers}, localURL={Documents/89-100.pdf}, year={2003}, publisher={The National Academies Press}, address={Washington, DC} }
  15. @article{Robins2001ER, title={Network models for social influence processes}, author={Robins, Garry and Pattison, Philippa and Elliott, Peter}, year={2001} URL={https://doi.org/10.1007/BF02294834}, localURL={Documents/Robins2001_Article_NetworkModelsForSocialInfluenc.pdf}, abstract={This paper generalizes thep* class of models for social network data to predict individual-level attributes from network ties. Thep* model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so constructp* models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees' reactions to aspects of the course found to relate to those of their network partners.} }
  16. @inproceedings{Ghorbani2016ManagingTC, title={Managing the commons: a simple model of the emergence of institutions through collective action}, author={Amineh Ghorbani and Giangiacomo Bravo}, year={2016} URL={https://www.thecommonsjournal.org/articles/10.18352/ijc.606/}, localURL={Documents/Ghorbani2016EmergentInstitutionsABM.pdf}, abstract={In this paper we present an abstract replication of institutional emergence patterns observed in common pool resource (CPR) problems. We used the ADICO grammar of institutions as the basic structure to model both agents’ strategies and institutions. Through an evolutionary process, agents modify their behaviours and eventually establish a management institution for their CPR system, leading to significant benefits both for them and for the commons as a whole. We showed that, even if our model has a high level of abstraction, by taking an evolutionary perspective and using the ADICO structure we are able to observe common institutional patterns. We confirmed that, even within this simplified environment, institutions significantly contributed to the sustainable management of common-pool resource systems. } }
  17. @article{ZHANG2006599, title="Model and empirical study on some collaboration networks", journal="Physica A: Statistical Mechanics and its Applications", volume="360", number="2", pages="599-616", year="2006", issn="0378-4371", doi="https://doi.org/10.1016/j.physa.2005.05.044", url="Documents/ModelAndEmpStudyOnCollabNets.pdf", localURL="Documents/ModelAndEmpStudyOnCollabNets.pdf", author="Pei-Pei Zhang and Kan Chen and Yue He and Tao Zhou and Bei-Bei Su and Yingdi Jin and Hui Chang and Yue-Ping Zhou and Li-Cheng Sun and Bing-Hong Wang and Da-Ren He", abstract="In this paper we present an empirical study of a few practical systems described by cooperation networks, and propose a model to understand the results obtained. We study four non-social systems, which are the Bus Route Networks of Beijing and Yangzhou, the Travel Route Network of China, Huai-Yang recipes of Chinese cooked food, and a social system, which is the Collaboration Network of Hollywood Actors. In order to explain the results related to the degree distribution, act-degree distribution and act-size distribution (especially about the degree distribution, which may be better fitted using a stretched exponential distribution (SED)), we suggest a simple model to show a possible evolutionary mechanism for the emergence of such networks. The analytic and numerical results obtained from the model are in good agreement with the empirical results." }
  18. @article{doi:10.1287/orsc.10.3.253, author={Frank, Kenneth A. and Fahrbach, Kyle}, title={Organization Culture as a Complex System: Balance and Information in Models of Influence and Selection}, journal={Organization Science}, volume={10}, number={3}, pages={253-277}, year={1999}, doi={10.1287/orsc.10.3.253}, URL={ https://doi.org/10.1287/orsc.10.3.253 }, localURL={Documents/OrgAsComplexSystem.pdf}, eprint={ https://doi.org/10.1287/orsc.10.3.253 }, abstract={ We define the complex system underlying organizational culture by incorporating the social-psychological principles of balance and information (B-I) into models of influence (changes in attitudes as a function of interaction) and selection (changes in interaction). We identify information based influence as a potential anchor for actors' sentiments so that they are not overwhelmed by normative influence. In the model of selection, we identify the pursuit of information as an important counterbalance to the effect of homophily (interacting with others like oneself). Using the tools of dynamic systems we show how our models generate the full range of equilibria of complex systems. Through simulations we also explore how our system responds to exogenous effects. } }

Model Assessment

  1. @article{WILLIAMS2020104831, title="Assessing model equifinality for robust policy analysis in complex socio-environmental systems", journal="Environmental Modelling & Software", volume="134", pages="104831", year="2020", issn="1364-8152", doi="https://doi.org/10.1016/j.envsoft.2020.104831", url="http://www.sciencedirect.com/science/article/pii/S1364815220308884", author="T.G. Williams and S.D. Guikema and D.G. Brown and A. Agrawal", keywords="Model calibration, Equifinality, Policy analysis, Evolutionary algorithm, Pattern-oriented modeling, Agent-based modeling", abstract="Equifinality—a situation in which multiple plausible explanations exist for a single outcome—presents a challenge for socio-environmental systems modeling. When equifinality is ignored in model calibration, subsequent policy analyses may mis-estimate the range of potential policy effects. In this paper, we present and demonstrate an approach—called DMC-RPA—for generating a set of diverse model calibrations (DMC) to enable more robust policy analysis (RPA). The optimization-based approach maximizes diversity in the model parameters and/or structural configurations to efficiently represent any equifinality in the model set. We demonstrate the approach for an agent-based model that is used to compare resilience-enhancing strategies in a smallholder farming system. Results over the set of diverse model calibrations demonstrate consistent policy effects, enabling stronger conclusions than a single model analysis. Going forward, this approach can be applied in the development of socio-environmental systems models to facilitate more robust policy analysis and inference.", localURL={Documents/Assessing model equifinality for robust policy analysis in complex socio-environmental systems.pdf} }

Network Analysis and Motif Detection

  1. @article{SPIRO2013130, title="Extended structures of mediation: Re-examining brokerage in dynamic networks", journal="Social Networks", volume="35", number="1", pages="130 - 143", year="2013", issn="0378-8733", doi="https://doi.org/10.1016/j.socnet.2013.02.001", url="http://www.sciencedirect.com/science/article/pii/S0378873313000087", author="Emma S. Spiro and Ryan M. Acton and Carter T. Butts", keywords="Brokerage, Social processes, Structural theory, Social networks, Structural dynamics, Coordination, Emergent multi-organizational networks", abstract="In this paper we revisit the concept of brokerage in social networks. We elaborate on the concept of brokerage as a process, identifying three distinct classes of brokerage behavior. Based on this process model, we develop a framework for measuring brokerage opportunities in dynamic relational data. Using data on emergent inter-organizational collaborations, we employ the dynamic brokerage framework to examine the relationship between organizational attributes and coordination in the evolving network. Comparing the findings of our process-based definition with traditional, static approaches, we identify important dimensions of organizational action that would be missed by the latter approach.", localurl="Documents/1-s2.0-S0378873313000087-main.pdf" }
  2. @article{Gould1989StructuresOM, title={Structures of Mediation: A Formal Approach to Brokerage in Transaction Networks}, author={Roger V. Gould and R. M. Fernandez}, journal={Sociological Methodology}, year={1989}, volume={19}, pages={89}, abstract={The concept of brokerage has gained considerable attention in recent years, but few researchers have attempted to specify what the phenomenon is. In this paper, we develop a theoretical conception of brokerage behavior in social systems characterized by the exchange or flow of resources. Building on the idea that any set of actors can be partitioned in a meaningful way into a set of mutually exclusive subgroups, we show that such a partition generates five formally, analytically, and intuitively distinct brokerage types or roles. We construct quantitative measures of each of these five types for actors in social networks and for whole systems, and show that statistical inference can be used to test whether occupancy of a brokerage position is the product of a random distribution of exchange relations or the product of underlying social structure.}, url={https://www.semanticscholar.org/paper/Structures-of-Mediation%3A-A-Formal-Approach-to-in-Gould-Fernandez/b2393930c73e6155b0991c63377f9fa21ef87635}, localurl={Documents/270949.pdf} }
  3. @article{WANG201396, title="Exponential random graph models for multilevel networks", journal="Social Networks", volume="35", number="1", pages="96 - 115", year="2013", issn="0378-8733", doi="https://doi.org/10.1016/j.socnet.2013.01.004", url="http://www.sciencedirect.com/science/article/pii/S0378873313000051", author="Peng Wang and Garry Robins and Philippa Pattison and Emmanuel Lazega", keywords="Multilevel networks, Exponential random graph models", abstract="Modern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives solely from common group membership. It is not plausible that such groups have no internal structure nor they have no links between each other. Networks provide a more complex representation of interdependence. Drawing on a small but crucial body of existing work, we present a general formulation of a multilevel network structure. We extend exponential random graph models (ERGMs) to multilevel networks, and investigate the properties of the proposed models using simulations which show that even very simple meso effects can create structure at one or both levels. We use an empirical example of a collaboration network about French cancer research elites and their affiliations (Lazega et al., 2006, Lazega et al., 2008) to demonstrate that a full understanding of the network structure requires the cross-level parameters. We see these as the first steps in a full elaboration for general multilevel network analysis using ERGMs." localURL={Documents/Wang_2013.pdf} }
  4. @article{SHORE2013116, title="Power laws and fragility in flow networks", journal="Social Networks", volume="35", number="1", pages="116 - 123", year="2013", issn="0378-8733", doi="https://doi.org/10.1016/j.socnet.2013.01.005", url="http://www.sciencedirect.com/science/article/pii/S0378873313000063", localURL={Documents/Shore_2013_network fragility.pdf}, author="Jesse Shore and Catherine J. Chu and Matt T. Bianchi", keywords="Network dynamics, Power law, Degree distribution, Flow networks, Network collapse, Resource networks, Network evolution", abstract="What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence." }
  5. @article {Milo824, author={Milo, R. and Shen-Orr, S. and Itzkovitz, S. and Kashtan, N. and Chklovskii, D. and Alon, U.}, title={Network Motifs: Simple Building Blocks of Complex Networks}, volume={298}, number={5594}, pages={824--827}, year={2002}, doi={10.1126/science.298.5594.824}, publisher={American Association for the Advancement of Science}, abstract={Complex networks are studied across many fields of science. To uncover their structural design principles, we defined network motifs,patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.}, issn={0036-8075}, localURL={Documents/NetworkMotifsSimpleBuildingBlocks.pdf}, URL={https://science.sciencemag.org/content/298/5594/824}, eprint={https://science.sciencemag.org/content/298/5594/824.full.pdf}, journal={Science} }
  6. @article{2009, title={MODA: An efficient algorithm for network motif discovery in biological networks}, author={Saeed Omidi and Falk Schreiber and Ali Masoudi-Nejad}, journal={Genes & Genetic Systems}, volume={84}, number={5}, pages={385-395}, year={2009}, abstract={In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/.} doi={10.1266/ggs.84.385} url={Documents/MODA-EfficientNetworkMotifDiscovery.pdf} }
  7. @Article{Kashani2009, author="Kashani, Zahra Razaghi Moghadam and Ahrabian, Hayedeh and Elahi, Elahe and Nowzari-Dalini, Abbas and Ansari, Elnaz Saberi and Asadi, Sahar and Mohammadi, Shahin and Schreiber, Falk and Masoudi-Nejad, Ali", title="Kavosh: a new algorithm for finding network motifs", journal="BMC Bioinformatics", year="2009", month="Oct", day="04", volume="10", number="1", pages="318", abstract="Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.", issn="1471-2105", doi="10.1186/1471-2105-10-318", url="Documents/Kavosh-NewAlgorithmForFindingNetworkMotifs.pdf" }
  8. @INPROCEEDINGS{5380881, author={P Ribeiro and F Silva and M Kaiser}, booktitle={2009 Fifth IEEE International Conference on e-Science}, title={Strategies for Network Motifs Discovery}, year={2009}, pages={80-87}, keywords={algorithm theory;data mining;network motifs discovery;e-Science data sets;workflow bottleneck;subgraph mining;graph isomorphism;motif detection algorithm;Computer networks;Complex networks;Biology computing;Sociology;Sequences;Proteins;Taxonomy;Neuroscience;Network address translation;Runtime;Network Motifs;Graph Mining;Algorithms;Complex Networks}, doi={10.1109/e-Science.2009.20}, ISSN={}, month={Dec}, abstract={Complex networks from domains like Biology or Sociology are present in many e-Science data sets. Dealing with networks can often form a workflow bottleneck as several related algorithms are computationally hard. One example is detecting characteristic patterns or “network motifs” – a problem involving subgraph mining and graph isomorphism. This paper provides a review and runtime comparison of current motif detection algorithms in the field. We present the strategies and the corresponding algorithms in pseudo-code yielding a framework for comparison. We categorize the algorithms outlining the main differences and advantages of each strategy. We finally implement all strategies in a common platform to allow a fair and objective efficiency comparison using a set of benchmark networks. We hope to inform the choice of strategy and critically discuss future improvements in motif detection.}, localurl={Documents/StrategiesForNetworkMotifDiscovery.pdf} }
  9. @article{BODIN2012430, title="Disentangling intangible social–ecological systems", journal="Global Environmental Change", volume="22", number="2", pages="430 - 439", year="2012", note="Adding Insult to Injury: Climate Change, Social Stratification, and the Inequities of Intervention", issn="0959-3780", doi="https://doi.org/10.1016/j.gloenvcha.2012.01.005", url="http://www.sciencedirect.com/science/article/pii/S0959378012000179", localurl={Documents/DisentanglingIntangibleSES.pdf} author="Örjan Bodin and Maria Tengö", keywords="Social–ecological systems, Transdisciplinary, Multi-theoretical framework, Network analysis, Motif, Environmental governance", abstract="Contemporary environmental challenges call for new research approaches that include the human dimension when studying the natural environment. In spite of the recent development of several conceptual frameworks integrating human society with nature, there has been less methodological and theoretical progress on how to quantitatively study such social–ecological interdependencies. We propose a novel theoretical framework for addressing this gap that partly builds on the rapidly growing interdisciplinary research on complex networks. The framework makes it possible to unpack, define and formalize ways in which societies and nature are interdependent, and to empirically link this to specific governance challenges and opportunities using a range of theories from both the social and natural sciences in an integrated way. At the core of the framework is a set of basic building blocks (motifs) that each represents a simplified but non-trivial social–ecological systems (SES) consisting of two social actors and two ecological resources. The set represents all possible patterns of interdependency in a SES. Each unique motif is characterized in terms of social and ecological connectivity, resource sharing, and resource substitutability. By aligning theoretical insights related to the management of common-pool resources, metapopulation dynamics, and the problem of fit in SES with the set of motifs, we demonstrate the multi-theoretical ability of the framework in a case study of a rural agricultural landscape in southern Madagascar. Several mechanisms explaining the inhabitants’ demonstrated ability to preserve their scattered forest patches in spite of strong pressures on land and forest resources are presented." }
  10. @article{COLETTO201722, title="Automatic controversy detection in social media: A content-independent motif-based approach", journal="Online Social Networks and Media", volume="3-4", pages="22 - 31", year="2017", issn="2468-6964", doi="https://doi.org/10.1016/j.osnem.2017.10.001", url="http://www.sciencedirect.com/science/article/pii/S2468696417300721", localurl={Documents/AutoControversyDetection.pdf} author="Mauro Coletto and Kiran Garimella and Aristides Gionis and Claudio Lucchese", keywords="Controversy detection, Polarization, Social network analysis, Twitter, Motif, Social media", abstract="Online social networks are becoming the primary medium by which people get informed, as they provide a forum for expressing ideas, contributing to public debates, and participating in opinion-formation processes. Among the topics discussed in Social Media, some lead to controversy. Identifying controversial topics is useful for exploring the space of public discourse and understanding the issues of current interest. Thus, a number of recent studies have focused on the problem of identifying controversy in social media mostly based on the analysis of textual content or rely on global network structure. Such approaches have strong limitations due to the difficulty of understanding natural language, especially in short texts, and of investigating the global network structure. In this work, we show that it is possible to detect controversy in social media by exploiting network motifs, i.e., local patterns of user interaction. The proposed approach allows for a language-independent and fine-grained analysis of user discussions and their evolution over time. Network motifs can be easily extracted both from user interactions and from the underlying social network, and they are conceptually simple to define and very efficient to compute. We assess the predictive power of motifs on a manually labeled twitter dataset. In fact, a supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features. Finally, thanks to the locality of motif patterns, we show that it is possible to monitor the evolution of controversy in a conversation over time thus discovering changes in user opinion." }
  11. @article{TOPIRCEANU2016167, title="Uncovering the fingerprint of online social networks using a network motif based approach", journal="Computer Communications", volume="73", pages="167 - 175", year="2016", note="Online Social Networks", issn="0140-3664", doi="https://doi.org/10.1016/j.comcom.2015.07.002", url="http://www.sciencedirect.com/science/article/pii/S0140366415002406", localurl={Documents/UncoveringFingerprintOnlineSocialNetworks.pdf} author="Alexandru Topirceanu and Alexandra Duma and Mihai Udrescu", keywords="Online social networks, Complex network topologies, Network motifs, Classification, Similarity", abstract="Complex networks facilitate the understanding of natural and man-made processes and are classified based on the concepts they model: biological, technological, social or semantic. The relevant subgraphs in these networks, called network motifs, are demonstrated to show core aspects of network functionality and can be used to analyze complex networks based on their topological fingerprint. We propose a novel approach of classifying social networks based on their topological aspects using motifs. As such, we define the classifiers for regular, random, small-world and scale-free topologies, and then apply this classification on empirical networks. We then show how our study brings a new perspective on differentiating between online social networks like Facebook, Twitter and Google Plus based on the distribution of network motifs over the fundamental topology classes. Characteristic patterns of motifs are obtained for each of the analyzed online networks and are used to better explain the functional properties behind how people interact online and to define classifiers capable of mapping any online network to a set of topological-communicational properties." }

Self-Organization in Networks

  1. @article{SFB, title={Life’s Information Hierarchy}, author={Flack, J.C.}, journal={Santa Fe Institute Bulletin}, year={2014}, month={April}, abstract={The explanation for the complex, multi-scale structure of biological and social systems lies in their manipulation of space and time to reduce uncertainty about the future.}, url={https://sfi-edu.s3.amazonaws.com/sfi-edu/production/uploads/resource_link_files/SFIB_2014_Flack_dc9359.pdf}, localurl={Documents/LIFE’S INFORMATION HIERARCHY_Flack_SantaFeInstituteBulleton_April_2014.pdf}
  2. @article{TheoryInBiosciences, title={The information theory of individuality}, author={Krakauer, David and Bertschinger, Nils and Olbrich, Eckehard and Flack, Jessica C. and Ay, Nihat}, journal={Theory in Biosciences}, volume={139}, issue={2}, pages={209-223}, year={2020}, month={Jun}, abstract={Despite the near universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. Here, we propose that individuals are aggregates that preserve a measure of temporal integrity, i.e., “propagate” information from their past into their futures. We formalize this idea using information theory and graphical models. This mathematical formulation yields three principled and distinct forms of individuality—an organismal, a colonial, and a driven form—each of which varies in the degree of environmental dependence and inherited information. This approach can be thought of as a Gestalt approach to evolution where selection makes figure-ground (agent–environment) distinctions using suitable information-theoretic lenses. A benefit of the approach is that it expands the scope of allowable individuals to include adaptive aggregations in systems that are multi-scale, highly distributed, and do not necessarily have physical boundaries such as cell walls or clonal somatic tissue. Such individuals might be visible to selection but hard to detect by observers without suitable measurement principles. The information theory of individuality allows for the identification of individuals at all levels of organization from molecular to cultural and provides a basis for testing assumptions about the natural scales of a system and argues for the importance of uncertainty reduction through coarse-graining in adaptive systems.}, doi={10.1007/s12064-020-00313-7}, url={https://doi.org/10.1007/s12064-020-00313-7}, localurl={Documents/Krakauer2020_Article_TheInformationTheoryOfIndividu.pdf}
  3. @article{PhysRevE.74.016108, title={Modeling self-organization of communication and topology in social networks}, author={Rosvall, M. and Sneppen, K.}, journal={Phys. Rev. E}, volume={74}, issue={1}, pages={016108}, numpages={6}, year={2006}, month={Jul}, abstract={This paper introduces a model of self-organization between communication and topology in social networks, with a feedback between different communication habits and the topology. To study this feedback, we let agents communicate to build a perception of a network and use this information to create strategic links. We observe a narrow distribution of links when the communication is low and a system with a broad distribution of links when the communication is high. We also analyze the outcome of chatting, cheating, and lying, as strategies to get better access to information in the network. Chatting, although only adopted by a few agents, gives a global gain in the system. Contrary, a global loss is inevitable in a system with too many liars.}, publisher={American Physical Society}, doi={10.1103/PhysRevE.74.016108}, url={Documents/ModelingSelfOrgAndCommInSocNets.pdf} localurl={Documents/ModelingSelfOrgAndCommInSocNets.pdf} }
  4. @article{PhysRevE.70.036106, title={Self-organization of collaboration networks}, author={Ramasco, Jose J. and Dorogovtsev, S. N. and Pastor-Satorras, Romualdo}, journal={Phys. Rev. E}, volume={70}, issue={3}, pages={036106}, numpages={10}, year={2004}, month={Sep}, abstract={We study collaboration networks in terms of evolving, self-organizing bipartite graph models. We propose a model of a growing network, which combines preferential edge attachment with the bipartite structure, generic for collaboration networks. The model depends exclusively on basic properties of the network, such as the total number of collaborators and acts of collaboration, the mean size of collaborations, etc. The simplest model defined within this framework already allows us to describe many of the main topological characteristics (degree distribution, clustering coefficient, etc.) of one-mode projections of several real collaboration networks, without parameter fitting. We explain the observed dependence of the local clustering on degree and the degree–degree correlations in terms of the “aging” of collaborators and their physical impossibility to participate in an unlimited number of collaborations.}, publisher={American Physical Society}, doi={10.1103/PhysRevE.70.036106}, localurl={Documents/SelfOrgOfCollabNets.pdf} }
  5. @article{BAGGIO201832, title="Managing ecological disturbances: Learning and the structure of social-ecological networks", journal="Environmental Modelling & Software", volume="109", pages="32 - 40", year="2018", issn="1364-8152", doi="https://doi.org/10.1016/j.envsoft.2018.08.002", url="http://www.sciencedirect.com/science/article/pii/S1364815217313075", localurl={Documents/ManagingEcoDisturbancesLearningSENs.pdf} author="J.A. Baggio and V. Hillis", keywords="Ecological disturbances, Environmental management, ABM, Learning, Social-ecological networks", abstract="Ecological disturbances (i.e. pests, fires, floods, biological invasions, etc.) are a critical challenge for natural resource managers. Land managers play a key role in altering the rate and extent of disturbance propagation. Ecological disturbances propagate across the landscape, while management strategies propagate across social networks of managers. Here we use an agent-based model to examine the joint diffusion of ecological disturbances and management strategies across a social-ecological network, accounting for the fundamental role of social-ecological feedbacks. We examine the management of a generic ecological disturbance as a function of different learning strategies and the social-ecological network. Our approach provides a general scaffold that can be modified to examine a variety of processes in which both social and ecological flows propagate across a social-ecological network. Our findings highlight the importance of full and accurate information to assess successful strategy, limited clustering and alignment between the social and the ecological system." }