A return to biased nets: new specifications and approximate Bayesian Inference*
In: The journal of mathematical sociology, S. 1-29
ISSN: 1545-5874
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In: The journal of mathematical sociology, S. 1-29
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 48, Heft 2, S. 129-171
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 46, Heft 1, S. 1-27
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 45, Heft 3, S. 135-147
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 43, Heft 1, S. 40-57
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 42, Heft 1, S. 17-36
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 40, Heft 1, S. 1-6
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 39, Heft 3, S. 174-202
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 24, Heft 4, S. 273-301
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 48, Heft 3, S. 311-339
ISSN: 1545-5874
In: The journal of mathematical sociology, Band 39, Heft 3, S. 163-167
ISSN: 1545-5874
In: Sociological methodology, Band 44, Heft 1, S. 273-321
ISSN: 1467-9531
Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.
Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention-designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs.
BASE
In: Political analysis: official journal of the Society for Political Methodology, the Political Methodology Section of the American Political Science Association, Band 21, Heft 4, S. 430-429
ISSN: 1047-1987
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Band 21, Heft 4, S. 430-448
ISSN: 1476-4989
Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention—designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs.