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Below results based on the criteria 'network'
Total number of records returned: 7
Degeneracy and Inference for Social Networks
Handcock, Mark S.
Random graph models
log-linear network model
Markov Chain Monte Carlo
Networks are a form of "relational data". Relational data arise in many social science fields and graph models are a natural approach to representing the structure of these relations. This framework has many applications including, for example, the structure of social networks, the behavior of epidemics, the interconnectedness of the WWW, and long-distance telephone calling patterns. We review stochastic models for such graphs, with particular focus on sexual and drug use networks. Commonly used Markov models were introduced by Frank and Strauss (1986) and were derived from developments in spatial statistics (Besag 1974). These models recognize the complex dependencies within relational data structures. To date, the use of graph models for networks has been limited by three interrelated factors: the complexity of realistic models, lack of use of simulation studies, and a poor understanding of the properties of inferential methods. In this talk we discuss these factors and the degeneracy of commonly promoted models. We also review the role of Markov Chain Monte Carlo (MCMC) algorithms for simulation and likelihood-based inference. These ideas are applied to a sexual relations network from Colorado Springs with the objective of understanding the social determinants of HIV spread. In this talk we focus on stochastic models for such graphs that can be used to represent the structural characteristics of the networks. In our applications, the nodes usually represent people, and the edges represent a specified relationship between the people.
Forecasting State Failure
We offer the first independent scholarly evaluation of the claims, forecasts, and causal inferences of the State Failure Task Force and their efforts to forecast when states will fail. This Task Force, set up at the behest of Vice President Gore in 1994, has been led by a group of distinguished academics working as consultants to the U.S. Government. State failure is a grave condition that includes civil wars, revolutionary wars, genocides, politicides, and adverse or disruptive regime transitions. State Failure Task Force reports and publications have received widespread attention in the media, in academia, and from public policy decision-makers. In this paper, we identify several methodological errors in the Task Force work that cause their reported forecast probabilities of conflict to be much too large, their causal inferences to be biased in unpredictable directions, and their claims of forecasting performance to be exaggerated. However, we also find that the Task Force has amassed the best and most carefully collected data on state failure in existence, and the required corrections, although very large in effect, are easy to implement. We also reanalyze their data with better statistical and other procedures and demonstrate how to improve forecasting performance to levels significantly greater than even corrected versions of their models. We hope that this work leads to better use of political science and statistical analyses in public policy, but most of the claims analyzed are also of direct relevance to ongoing scholarly debates in political science, public health, and other disciplines.
The Structure of Signaling: A Combinatorial Optimization Model with Network-Dependent Estimation
Esterling, Kevin M.
This paper examines the relationship between lobbyists' contact-making behavior and their long-term access to the government. Specifically: 1) Do lobbyists establish social contacts in an individually-rational manner to best receive information from each other? And, 2) does the resulting network position condition their access to the government? We begin by wedding rational choice models to network analysis with a formal model of lobbyists' choice of contacts in a network, adopting the classic combinatorial optimization approach of Boorman (1975). The model predicts that when the demand for political information is low, a cocktail equilibrium prevails: lobbyists will invest their time in gaining "weak tie" acquaintances rather than in gaining "strong tie" trusted partners. When the demand for information in a policy domain is high, then both cocktail equilibria and "chum" equilibria (all strong-tie networks) prevail. We then turn to an empirical analysis of lobbyist contact-making and access, using the data of Laumann and Knoke in The Organizational State. We analyze the communication structure of the policy domains in health policy, using count data models that are adjusted for "structural autocorrelation" by the networks we study. The results support the cocktail equilibrium hypothesis, and offer a result that portends rich questions for future research: Washington lobbyists appear to overinvest in strong ties, in general reducing their credibility with the government in the long-term, as well as reducing the informational efficiency of the overall communication network.
Modeling Foreign Direct Investment as a Longitudinal Social Network
foreign direct investment
social network data
An extensive literature in international and comparative political economy has focused on the how the mobility of capital affects the ability of governments to tax and regulate firms. The conventional wisdom holds that governments are in competition with each other to attract foreign direct investment (FDI). Nation-states observe the fiscal and regulatory decisions of competitor governments, and are forced to either respond with policy changes or risk losing foreign direct investment, along with the politically salient jobs that come with these investments. The political economy of FDI suggests a network of investments with complicated dependencies. We propose an empirical strategy for modeling investment patterns in 24 advanced industrialized countries from 1985-2000. Using bilateral FDI data we estimate how increases in flows of FDI affect the flows of FDI in other countries. Our statistical model is based on the methodology developed by Westveld & Hoff (2007). The model allows the temporal examination of each notion's activity level in investing, attractiveness to investors, and reciprocity between pairs of nations. We extend the model by treating the reported inflow and outflow data as independent replicates of the true value and allowing for a mixture model for the fixed effects portion of the network model. Using a fully Bayesian approach, we also impute missing data within the MCMC algorithm used to fit the model.
Cosponsorship in the U.S. Senate: A Multilevel Approach to Detecting Subtle Social Predictors of Legilslative Support
social network analysis
Why do members of Congress choose to cosponsor legislation proposed by their colleagues and what can we learn from their patterns of cosponsorship? To answer these questions properly requires models that respect the relational nature of the relevant data and the resulting interdependence among observations. We show how the inclusion of carefully selected random effects can capture network-type dependence, allowing us to more confidently investigate senators' propensity to support colleagues' proposals. To illustrate, we examine whether certain social factors such as demographic similarities, opportunities for interaction, and institutional roles are associated with varying odds of cosponsorship during the 2003-04 (108th) Senate.
Invaluable Involvement: Purposive Interest Group Networks in the 21st Century
We present the first comprehensive social network analysis of purposive and coordinated interest group relationships. We utilize a network measure based on cosigner status to United States Supreme Court amicus curiae, or friend of the court briefs. The illuminated structures lend insight into the central players and overall formation of the network over the first seven years of the 21st century. We find that the majority of interest groups primarily partake in coalition strategies with other groups of similar policy interest and ideological character. This is in contrast to previous literature that focused only on one or the other. Network analysis provides evidence, for example, that the National Wildlife Foundation, the National Association of Criminal Defense Lawyers and the American Civil Liberties Union are all particularly strong groups, but exploit different central roles.
Modeling History Dependence in Network-Behavior Coevolution
Spatial interdependence--the dependence of outcomes in some units on those in others--is substantively and theoretically ubiquitous and central across the social sciences. Spatial association is also omnipresent empirically. However, spatial association may arise from three importantly distinct processes: common exposure of actors to exogenous external and internal stimuli, interdependence of outcomes/behaviors across actors (contagion), and/or the putative outcomes may affect the variable along which the clustering occurs (selection). Accurate inference about any of these processes generally requires an empirical strategy that addresses all three well. From a spatial-econometric perspective, this suggests spatiotemporal empirical models with exogenous covariates (common exposure) and spatial lags (contagion), with the spatial weights being endogenous (selection). From a longitudinal network-analytic perspective, we can identify the same three processes as potential sources of network effects and network formation. From that perspective, actors' self-selection into networks (by, e.g., behavioral homophily) and actors' behavior that is contagious through those network connections likewise demands theoretical and empirical models in which networks and behavior coevolve over time. This paper begins building such modeling by, on the theoretical side, extending a Markov type-interaction model to allow endogenous tie-formation, and, on the empirical side, merging a simple spatial-lag logit model of contagious behavior with a simple p-star logit model of network formation, building this synthetic discrete-time empirical model from the theoretical base of the modified Markov type-interaction model. One interesting consequence of network-behavior coevolution--identically: endogenous patterns of spatial interdependence--emphasized here is how it can produce history-dependent political dynamics, including equilibrium phat and path dependence (Page 2006). The paper explores these implications, and then concludes with a preliminary demonstration of the strategy applied to alliance formation and conflict behavior among the great powers in the first half of the twentieth century.