Triadic Temporal Exponential Random Graph Models (TTERGM)

TL;DR

This paper introduces Triadic Temporal ERGMs (TTERGM), a new model that incorporates triadic relationships and social learning theories into temporal network modeling, improving prediction accuracy.

Contribution

The paper proposes TTERGM, integrating hierarchical network relationships and social learning into temporal ERGMs, enhancing model fidelity and predictive performance.

Findings

TTERGM outperforms benchmark methods on GitHub network data.

Inclusion of triadic and social learning factors improves model accuracy.

Monte Carlo maximum likelihood estimation effectively approximates new parameters.

Abstract

Temporal exponential random graph models (TERGM) are powerful statistical models that can be used to infer the temporal pattern of edge formation and elimination in complex networks (e.g., social networks). TERGMs can also be used in a generative capacity to predict longitudinal time series data in these evolving graphs. However, parameter estimation within this framework fails to capture many real-world properties of social networks, including: triadic relationships, small world characteristics, and social learning theories which could be used to constrain the probabilistic estimation of dyadic covariates. Here, we propose triadic temporal exponential random graph models (TTERGM) to fill this void, which includes these hierarchical network relationships within the graph model. We represent social network learning theory as an additional probability distribution that optimizes Markov chains in the graph vector space. The new parameters are then approximated via Monte Carlo maximum likelihood estimation. We show that our TTERGM model achieves improved fidelity and more accurate predictions compared to several benchmark methods on GitHub network data.