Why Legislative Networks? Analyzing Legislative Network Formation

2017 ◽  
Vol 7 (3) ◽  
pp. 505-522 ◽  
Author(s):  
Stefan Wojcik
Keyword(s):  
Political Parties ◽  
Network Formation ◽  
Contemporary Literature ◽  
Party Organization ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Personal Traits ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.

A Weighted Network Modeling Approach for Analyzing Product Competition

2020 ◽  
Author(s):  
Yaxin Cui ◽  
Faez Ahmed ◽  
Zhenghui Sha ◽  
Lijun Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Network Theory ◽  
Network Modeling ◽  
Network Models ◽  
Social Aspects ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social ◽  
Product Competition

Abstract Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affect which cars are considered together and which cars are finally bought by customers. Unlike past work, where most systems have been studied with the assumption that relationships between competitors are binary (i.e., whether a relationship exists or not), we allow relationships to take strengths (i.e., how strong a relationship is). Specifically, we use valued Exponential Random Graph Models and show that our approach provides a significant improvement over the baselines in predicting product co-considerations as well as in the validation of market share. This is also the first attempt to study aggregated purchase preference and car competition using valued directed networks.

scholarly journals Diagnosing Multicollinearity in Exponential Random Graph Models

2017 ◽  
Author(s):  
Scott W Duxbury
Keyword(s):  
Random Graph ◽  
Standard Errors ◽  
Exponential Random Graph Models ◽  
Multilevel Regression ◽  
Graph Models ◽  
Network Characteristics ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social ◽  
Inflation Factor

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, skewing coefficients, biasing standard errors, and yielding inconsistent model estimates. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.

Comparing membership interest group networks across space and time, size, issue and industry

2015 ◽  
Vol 3 (1) ◽  
pp. 78-97 ◽  
Author(s):  
JANET M. BOX-STEFFENSMEIER ◽  
DINO P. CHRISTENSON
Keyword(s):  
Interest Groups ◽  
Network Formation ◽  
Cooperative Behavior ◽  
Methodological Approach ◽  
Exponential Random Graph Models ◽  
Political Organizations ◽  
Graph Models ◽  
Random Graph Models ◽  
Professional Groups ◽  
Exponential Random Graph

AbstractWe compare and contrast the network formation of interest groups across industry and issue area. We focus on membership interest groups, which by virtue of representing the interests of voluntary members face particular organizational and maintenance constraints. To reveal their cooperative behavior we build a network dataset based on cosigner status to United States Supreme Court amicus curiae briefs and analyze it with exponential random graph models and multidimensional scaling. Our methodological approach culminates in a clear and compact spatial representation of network similarities and differences. We find that while many of the same factors shape membership networks, religious, labor, and political organizations do not share the same structure as each other or as the business, civic and professional groups.

scholarly journals Diagnosing Multicollinearity in Exponential Random Graph Models

2018 ◽  
Author(s):  
Scott W Duxbury
Keyword(s):  
Random Graph ◽  
Standard Errors ◽  
Exponential Random Graph Models ◽  
Multilevel Regression ◽  
Graph Models ◽  
Network Characteristics ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social ◽  
Inflation Factor

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, skewing coefficients, biasing standard errors, and yielding inconsistent model estimates. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.

scholarly journals Understanding network formation in strategy research: Exponential random graph models

2015 ◽  
Vol 37 (1) ◽  
pp. 22-44 ◽  
Author(s):  
Ji Youn Rose Kim ◽  
Michael Howard ◽  
Emily Cox Pahnke ◽  
Warren Boeker
Keyword(s):  
Random Graph ◽  
Network Formation ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Strategy Research ◽  
Random Graph Models ◽  
Exponential Random Graph

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 Seed exchange networks, ethnicity, and sorghum diversity

2015 ◽  
Vol 113 (1) ◽  
pp. 98-103 ◽  
Author(s):  
Vanesse Labeyrie ◽  
Mathieu Thomas ◽  
Zachary K. Muthamia ◽  
Christian Leclerc
Keyword(s):  
Exponential Random Graph Models ◽  
Graph Models ◽  
Exchange Networks ◽  
Seed Exchange ◽  
Crop Genetic Diversity ◽  
Random Graph Models ◽  
Kinship System ◽  
Exponential Random Graph ◽  
The Social ◽  
The Impact

Recent studies investigating the relationship between crop genetic diversity and human cultural diversity patterns showed that seed exchanges are embedded in farmers’ social organization. However, our understanding of the social processes involved remains limited. We investigated how farmers’ membership in three major social groups interacts in shaping sorghum seed exchange networks in a cultural contact zone on Mount Kenya. Farmers are members of residence groups at the local scale and of dialect groups clustered within larger ethnolinguistic units at a wider scale. The Chuka and Tharaka, who are allied in the same ethnolinguistic unit, coexist with the Mbeere dialect group in the study area. We assessed farmers’ homophily, propensity to exchange seeds with members of the same group, using exponential random graph models. We showed that homophily is significant within both residence and ethnolinguistic groups. At these two levels, homophily is driven by the kinship system, particularly by the combination of patrilocal residence and ethnolinguistic endogamy, because most seeds are exchanged among relatives. Indeed, residential homophily in seed exchanges results from local interactions between women and their in-law family, whereas at a higher level, ethnolinguistic homophily is driven by marriage endogamy. Seed exchanges and marriage ties are interrelated, and both are limited between the Mbeere and the other groups, although frequent between the Chuka and Tharaka. The impact of these social homophily processes on crop diversity is discussed.

scholarly journals Exponential random graph models for little networks

2021 ◽  
Vol 64 ◽  
pp. 225-238
Author(s):  
George G. Vega Yon ◽  
Andrew Slaughter ◽  
Kayla de la Haye
Keyword(s):  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph
Sign in / Sign up

Export Citation Format

Share Document