# Varying-coefficient models for dynamic networks

## TL;DR

This paper introduces VCERGM, a flexible and interpretable statistical model for dynamic networks that captures temporal and structural dependencies, enabling efficient inference and hypothesis testing.

## Contribution

The paper develops VCERGM, a novel varying-coefficient exponential random graph model for dynamic networks, with maximum pseudo-likelihood fitting and bootstrap hypothesis testing.

## Key findings

- Successfully applied to US Congress co-voting network
- Analyzed resting-state brain connectivity data
- Demonstrated advantages over existing methods

## Abstract

Dynamic networks are commonly used in applications where relational data is observed over time. Statistical models for such data should capture not only the temporal dependencies between networks observed in time, but also the structural dependencies among the nodes and edges in each network. As a consequence, effectively making inference on dynamic networks is a computationally challenging task, and many models established for dynamic networks are intractable even for moderately sized networks. In this paper, we propose and investigate a family of dynamic network models, known as varying-coefficient exponential random graph models (VCERGMs), that characterize the evolution of network topology through smoothly varying parameters in an exponential family of distributions. The VCERGM provides an interpretable dynamic network model that enables the inference of temporal heterogeneity in a dynamic network. We establish how to fit the VCERGM through maximum pseudo-likelihood techniques, and thus provide a computationally tractable method for statistical inference of complex dynamic networks. We furthermore devise a bootstrap hypothesis testing framework for testing the temporal heterogeneity of an observed dynamic network sequence. We apply the VCERGM to the US Congress co-voting network and a resting-state brain connectivity case study and show that our method provides relevant and interpretable patterns describing each data set. Comprehensive simulation studies demonstrate the advantages of our proposed method over existing methods.