Estimating Network Models using Neural Networks

• URL: http://arxiv.org/abs/2502.01810v1
• Date: Mon, 03 Feb 2025 20:41:06 GMT
• Title: Estimating Network Models using Neural Networks
• Authors: Angelo Mele,
• Abstract summary: 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.
• Score: 0.0
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• 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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