A note on perfect simulation for Exponential Random Graph Models

In this paper, we propose a perfect simulation algorithm for the Exponential Random Graph Model, based on the Coupling from the past method of Propp and Wilson (1996). We use a Glauber dynamics to construct the Markov Chain and we prove the monotonicity of the ERGM for a subset of the parametric space. We also obtain an upper bound on the running time of the algorithm that depends on the mixing time of the Markov chain.

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Inferential Network Analysis with Exponential Random Graph Models

Political Analysis ◽

10.1093/pan/mpq037 ◽

2011 ◽

Vol 19 (1) ◽

pp. 66-86 ◽

Cited By ~ 213

Author(s):

Skyler J. Cranmer ◽

Bruce A. Desmarais

Keyword(s):

Network Analysis ◽

Random Graph ◽

Graph Model ◽

Network Data ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Model ◽

General Acceptance ◽

Random Graph Models ◽

Exponential Random Graph

Methods for descriptive network analysis have reached statistical maturity and general acceptance across the social sciences in recent years. However, methods for statistical inference with network data remain fledgling by comparison. We introduce and evaluate a general model for inference with network data, the Exponential Random Graph Model (ERGM) and several of its recent extensions. The ERGM simultaneously allows both inference on covariates and for arbitrarily complex network structures to be modeled. Our contributions are three-fold: beyond introducing the ERGM and discussing its limitations, we discuss extensions to the model that allow for the analysis of non-binary and longitudinally observed networks and show through applications that network-based inference can improve our understanding of political phenomena.

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Antecedents of Border Management Network in El Paso, Texas: An Exponential Random Graph Model

Journal of Homeland Security and Emergency Management ◽

10.1515/jhsem-2016-0011 ◽

2018 ◽

Vol 15 (3) ◽

Cited By ~ 1

Author(s):

Jungwon Yeo

Keyword(s):

Random Graph ◽

El Paso ◽

Graph Model ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Model ◽

Random Graph Models ◽

Border Regions ◽

Exponential Random Graph ◽

Management Context

AbstractDespite the growing interest in interorganizational border management, relatively little is known about antecedents that drive such coordination efforts emerging in and around border regions. This case study uses exponential random graph models to test hypotheses about the antecedents of a border management coordination network in El Paso, Texas. The analysis demonstrates that actors tend to build tightly closed relationships through bonding and clustering, while also seeking cross-sectoral partnerships. In addition, actors tend to build ties with public organizations, and with organizations that represent regional interests/issues in the border management context. The research discusses the findings and offers some policy and administrative implications to enhance actor relationships within the border management network.

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Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model

Political Analysis ◽

10.1017/pan.2017.23 ◽

2018 ◽

Vol 26 (1) ◽

pp. 3-19 ◽

Cited By ~ 7

Author(s):

Janet M. Box-Steffensmeier ◽

Dino P. Christenson ◽

Jason W. Morgan

Keyword(s):

Random Graph ◽

Network Formation ◽

Unobserved Heterogeneity ◽

Graph Model ◽

Monte Carlo Analysis ◽

Exponential Random Graph Models ◽

Graph Models ◽

Random Graph Model ◽

Random Graph Models ◽

Exponential Random Graph

In the study of social processes, the presence of unobserved heterogeneity is a regular concern. It should be particularly worrisome for the statistical analysis of networks, given the complex dependencies that shape network formation combined with the restrictive assumptions of related models. In this paper, we demonstrate the importance of explicitly accounting for unobserved heterogeneity in exponential random graph models (ERGM) with a Monte Carlo analysis and two applications that have played an important role in the networks literature. Overall, these analyses show that failing to account for unobserved heterogeneity can have a significant impact on inferences about network formation. The proposed frailty extension to the ERGM (FERGM) generally outperforms the ERGM in these cases, and does so by relatively large margins. Moreover, our novel multilevel estimation strategy has the advantage of avoiding the problem of degeneration that plagues the standard MCMC-MLE approach.

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On the phase transition curve in a directed exponential random graph model

Advances in Applied Probability ◽

10.1017/apr.2018.13 ◽

2018 ◽

Vol 50 (01) ◽

pp. 272-301 ◽

Cited By ~ 1

Author(s):

David Aristoff ◽

Lingjiong Zhu

Keyword(s):

Phase Transition ◽

Free Energy ◽

Random Graph ◽

Graph Model ◽

Transition Curve ◽

Exponential Random Graph Models ◽

Normalization Constant ◽

Unusual Behavior ◽

Random Graph Model ◽

Exponential Random Graph

Abstract We consider a family of directed exponential random graph models parametrized by edges and outward stars. Much of the important statistical content of such models is given by the normalization constant of the models, and, in particular, an appropriately scaled limit of the normalization, which is called the free energy. We derive precise asymptotics for the normalization constant for finite graphs. We use this to derive a formula for the free energy. The limit is analytic everywhere except along a curve corresponding to a first-order phase transition. We examine unusual behavior of the model along the phase transition curve.

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De facto regulatory decision-making processes in telecommunications regulation: explaining influence relationships with exponential random graph models

Journal of Public Policy ◽

10.1017/s0143814x18000272 ◽

2018 ◽

Vol 40 (1) ◽

pp. 144-170 ◽

Cited By ~ 2

Author(s):

Camilo Ignacio González ◽

Koen Verhoest

Keyword(s):

Decision Making ◽

Random Graph ◽

Graph Model ◽

Exponential Random Graph Models ◽

Random Graph Model ◽

Network Characteristics ◽

Regulatory Decision ◽

Power Resources ◽

Exponential Random Graph ◽

Regulatory Decisions

AbstractResearch on regulation has focussed on explaining the independence of sector regulators and assessing the effects of regulations on markets. This article broadens the scope of such research by studying and explaining how regulatory actors interact at the de facto level in a multi-actor regulatory arrangement when making regulatory decisions in the telecommunications sector of Colombia. We propose that regulatory decisions depend on the manner in which actors influence each other. In this article, we are not only focussing on the policy outcome itself but also on the regulatory decision-making process. We performed a social network analysis and used an exponential random graph model to analyse the data. Our findings suggest that actors’ level of influence is affected by the access they have to other organisations, the divergence of positions they have with these other organisations and the power resources of an organisation. In addition, there are structural network characteristics that affect regulatory decisionmaking.

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