Dynamic Social Network Modeling and Analysis Workshop Summary and Papers (2003) / Chapter Skim
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Part I: Workshop Summary
Part I: Workshop Summary
Pages 1-14
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PART I Workshop Summary
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Social Network Theory Perspectives, (2) Dynamic Social Networks, (3)
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First, traditional social network analysis is "data greedy" very detailed data are required on all participants. Questions to be addressed in the analysis of these data concern how to estimate the data from high-level indicators, how sensitive the measures are to missing data, and how network data can be collected rapidly and/or automatically.
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How well do the different analytical techniques and algorithms "scale up" to large networks with hundreds or thousands of actors and multiple types of relations? Perhaps a more useful phrasing of this question is, Under what conditions andfor which analytical purposes do models of social networks scale up, and how well do existing techniques deal with uncertainty in information?
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Multiagent network models are featured in the dynamic network analysis of Kathleen Carley and in the computational modeling of Michael Macy and his colleagues. Carley has formulated a highly distinctive approach to the dynamic modeling of social networks.
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. Morris linked network data to network simulation by means of a statistical modeling framework: statistical models for random graphs as implemented via the Markov chain Monte Carlo (MCMC)
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Themes The papers in this session present methodological developments at the forefront of efforts to construct statistical models and metrics for understanding social networks. Although a number of papers in the other sessions also contributed significantly to this effort, a key distinction between these papers and those in the other sessions is that they focus on what can be learned if we only have network data.
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Klovdahl, Social Networks in Contemporary Societies 2. David Jensen, Data Mining in Social Networks (coauthor Jennifer Neville)
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Jensen and Neville joined social network analysis with data mining and related techniques of machine learning and knowledge discovery in order to investigate large networks. At the intersection of statistics, databases, artificial intelligence, and visualization, data mining techniques have been extended to relational data.
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Formulation of Models Networks "Plus": Generalized Relational Structures The social network community has often found it useful to view social networks as "skeletal" abstractions of a much richer social reality. An important question, though, is whether the extent to which attempts to model and quantify network properties can rely on the network observations alone or whether they would instead be enhanced by additional information about actors and ties and their embedding in other social forms (the constellation of which might be termed "generalized relational data structures".
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A number of statistical models that have been developed for social networks of medium size have attempted to express network structure as the outcome of regularities in interactive interpersonal processes at a "local" level. Can we extend the focus of such statistical modeling approaches to develop theoretically principled and testable models for social networks at a larger scale and in the process evaluate some of the claims emerging from the more descriptive analyses?
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who may strategically misreport their ties. Methods for analyzing network data exhibiting these properties include intensive application of multiple analytic procedures to compensate for problems of data quality (see Freeman's paper and related discussion in Session I above)
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One of the core features of social networks is arguably their potential to self-organize, which is especially likely in response to an intervention. Research presented at this workshop illustrates the potential for network models, both simulation and mathematical, to be used to foreshadow the probable network response to various types of interventions such as the removal of a node that is high in centrality or cognitive load, a "key player." This work suggests that, to be effective, strategies for altering networks need to be tailored to the processes by which the networks change, recruit new members, and diffuse goods, messages, or services.
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