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My friend far, far away: a random field approach to exponential random graph models
Vincent Boucher1, Ismael Mourifié2
1Department of Economics, Université Laval, 1025, Avenue des Sciences-Humaines, Quebec City, Quebec, G1V 0A6, Canada.
The Econometrics Journal
|April 23, 2019
Summary
This study analyzes network formation models in large populations. A logit-based estimator can reliably recover network parameters from observed social networks, even with weak homophily.
Area of Science:
- Computational Social Science
- Network Science
- Econometrics
Background:
- Understanding social network formation is crucial.
- Exponential random graph models (ERGM) are widely used.
- Parameter recovery from large networks is challenging.
Purpose of the Study:
- To explore asymptotic properties of strategic network formation models.
- To recover individual utility function parameters from large social network data.
- To evaluate a logit-based estimator for network parameter recovery.
Main Methods:
- Analysis of undirected exponential random graph models.
- Development and evaluation of a logit-based estimator.
- Asymptotic analysis under weak homophily conditions.
Main Results:
- The logit-based estimator is shown to be coherent, consistent, and asymptotically normal.
- The method requires minimal computation time.
- The estimator is easily implementable using standard statistical software.
Conclusions:
- The proposed logit-based estimator offers an efficient and reliable method for network parameter recovery.
- This approach is valuable for analyzing large-scale social network data.
- The method was successfully applied to the Add Health database.
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