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Estimating Network Models using Neural Networks

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  • Angelo Mele

Abstract

Exponential random graph models (ERGMs) are very flexible for modeling network formation but pose difficult estimation challenges due to their intractable normalizing constant. Existing methods, such as MCMC-MLE, rely on sequential simulation at every optimization step. We propose a neural network approach that trains on a single, large set of parameter-simulation pairs to learn the mapping from parameters to average network statistics. Once trained, this map can be inverted, yielding a fast and parallelizable estimation method. The procedure also accommodates extra network statistics to mitigate model misspecification. Some simple illustrative examples show that the method performs well in practice.

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  • Angelo Mele, 2025. "Estimating Network Models using Neural Networks," Papers 2502.01810, arXiv.org.
    • Handle: RePEc:arx:papers:2502.01810
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    References listed on IDEAS

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    1. Angelo Mele & Lingjiong Zhu, 2023. "Approximate Variational Estimation for a Model of Network Formation," The Review of Economics and Statistics, MIT Press, vol. 105(1), pages 113-124, January.
    2. Angelo Mele, 2017. "A Structural Model of Dense Network Formation," Econometrica, Econometric Society, vol. 85, pages 825-850, May.
    3. Vincent Boucher & Ismael Mourifié, 2017. "My friend far, far away: a random field approach to exponential random graph models," Econometrics Journal, Royal Economic Society, vol. 20(3), pages 14-46, October.
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