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arXiv · 2407.12175

Temporal Configuration Model: Statistical Inference and Spreading Processes

Thien-Minh Le·Hali Hambridge·Jukka-Pekka Onnela
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Abstract

We introduce a family of parsimonious network models that are intended to generalize the configuration model to temporal settings. We present consistent estimators for the model parameters and perform numerical simulations to illustrate the properties of the estimators on finite samples. We also develop analytical solutions for basic and effective reproductive numbers for the early stage of discrete-time SIR spreading process. We apply three distinct temporal configuration models to empirical student proximity networks and compare their performance.

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Thien-Minh Le, Hali Hambridge, Jukka-Pekka Onnela. 2024-07-16. Temporal Configuration Model: Statistical Inference and Spreading Processes. https://arxiv.org/abs/2407.12175

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