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Diagnosing Multicollinearity in Exponential Random Graph Models

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  • Scott W. Duxbury

Abstract

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past 10 years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, yielding inconsistent model estimates and problematizing inference from parameters. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor (VIF) from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the VIFs for each model. The distribution of VIFs is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between VIFs 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.

Suggested Citation

  • Scott W. Duxbury, 2021. "Diagnosing Multicollinearity in Exponential Random Graph Models," Sociological Methods & Research, , vol. 50(2), pages 491-530, May.
    • Handle: RePEc:sae:somere:v:50:y:2021:i:2:p:491-530
    DOI: 10.1177/0049124118782543
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    1. Hu, Yue & Dai, Liang & Fuellhart, Kurt & Witlox, Frank, 2025. "Unraveling policy effects on airline networks in China: A temporal exponential random graph framework for slot control and route entry restrictions," Transport Policy, Elsevier, vol. 171(C), pages 882-895.
    2. Elina H. Hwang & Xitong Guo & Yong Tan & Yuanyuan Dang, 2022. "Delivering Healthcare Through Teleconsultations: Implications for Offline Healthcare Disparity," Information Systems Research, INFORMS, vol. 33(2), pages 515-539, June.
    3. Rotolo, Daniele & Natalicchio, Angelo & Porter, Alan L. & Schoeneck, David J., 2025. "Emerging technologies, institutional groups and inter-organisational networks: The case of microneedles," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    4. Binbin Zheng & Jiahe Wang & Shuhu Feng & Han Yang & Wensong Wang & Tingting Feng & Tianyu Hu, 2024. "A new, fast, and accurate algorithm for predicting soil slope stability based on sparrow search algorithm-back propagation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(1), pages 297-319, January.
    5. Mathias Heidinger & Fabian Wenner & Sebastian Sager & Paul Sussmann & Alain Thierstein, 2023. "Where do knowledge-intensive firms locate in Germany?—An explanatory framework using exponential random graph modeling [Welche Standorte wählen wissensintensive Unternehmen in Deutschland? – Ein Erklärungsrahmen mit exponential random graph modeli," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 43(1), pages 101-124, April.
    6. Awais Ur Rehman & Saqib Farid & Muhammad Abubakr Naeem, 2022. "The link between corporate governance, corporate social sustainability and credit risk of Islamic bonds," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 18(12), pages 5990-6014, May.

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