scholarly journals Inferential Network Analysis with Exponential Random Graph Models

2011 ◽  
Vol 19 (1) ◽  
pp. 66-86 ◽  
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.

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

2018 ◽  
Vol 15 (3) ◽  
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.

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

2018 ◽  
Vol 26 (1) ◽  
pp. 3-19 ◽  
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.

scholarly journals The performance of permutations and exponential random graph models when analyzing animal networks

2020 ◽  
Vol 31 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Julian C Evans ◽  
David N Fisher ◽  
Matthew J Silk
Keyword(s):  
Social Network ◽  
Random Graph ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

Μeasuring and Visualizing Coders’ Reliability: New Approaches and Guidelines From Experimental Data

2020 ◽  
pp. 004912412092619
Author(s):  
Iasonas Lamprianou
Keyword(s):  
Experimental Data ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
New Approach ◽  
Random Graph Models ◽  
Exponential Random Graph

This study investigates inter- and intracoder reliability, proposing a new approach based on social network analysis (SNA) and exponential random graph models (ERGM). During a recent exit poll, the responses of voters to two open-ended questions were recorded. A coding experiment was conducted where a group of coders coded a sample of text segments. Analyzing the data, we show that the proposed SNA/ERGM method extends significantly our analytical leverage, beyond what popular tools such as Krippendorff’s α and Fleiss’s κ have to offer. The reliability of coding for individual coders differed significantly for the two questions although they were very similar and the same codebook was used. We conclude that the main advantages of the proposed SNA/ERGM method are the intuitive visualizations and the nuanced measurements. Detailed guidelines are provided for practitioners who would like to use the proposed method in operational settings.

scholarly journals A note on perfect simulation for Exponential Random Graph Models

2020 ◽  
Vol 24 ◽  
pp. 138-147 ◽  
Author(s):  
Andressa Cerqueira ◽  
Aurélien Garivier ◽  
Florencia Leonardi
Keyword(s):  
Markov Chain ◽  
Random Graph ◽  
Mixing Time ◽  
Graph Model ◽  
Glauber Dynamics ◽  
Perfect Simulation ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Model ◽  
Exponential Random Graph

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.

scholarly journals Social network analysis of lodging establishments in Tiradentes (MG): Profile and evidence of relationships genesis

2020 ◽  
Vol 22 (3) ◽  
pp. 424-445
Author(s):  
Geraldo Magela Rodrigues De Vasconcelos ◽  
Gustavo Melo-Silva ◽  
Velcimiro Inácio Maia
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

A análise de redes sociais (ARS) constitui um grande avanço na pesquisa em turismo ao revelar as características das relações estabelecidas, apresentando suas estruturas e propriedades. Este trabalho objetivou caracterizar e analisar a rede de cooperação formada entre proprietários de pousadas em Tiradentes-MG. Com o objetivo de explorar o contexto da pesquisa, foram realizadas entrevistas junto a sete proprietários de pousadas. Posteriormente, para a coleta dos dados, foi aplicado um questionário aos proprietários. Por meio da técnica da “bola de neve” foi gerada a rede de cooperação. A partir disso, utilizou-se das técnicas da ARS, com ênfase nas métricas descritivas e pela modelagem de grafos aleatórios da família exponencial (ERGM - Exponential Random Graph Models). Com isso, foi possível caracterizar a rede de cooperação e analisar suas propriedades. A rede possui 54 pousadas, com uma densidade geral baixa, pois há apenas 4,7% de relações possíveis. Foram identificadas 17 pousadas centrais e 37 periféricas. No subgrupo central, a densidade foi de 15,4% e no subgrupo periférico, de apenas 2,6%. A rede de cooperação observada apresentou homofilia por gênero e por procedência dos proprietários. Os resultados da modelagem ERGM permitiram explicações probabilísticas em termos de atributos endógenos dos proprietários, como gênero e procedência.

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