Modeling and detecting change in temporal networks via a dynamic degree corrected stochastic block model

TL;DR

This paper introduces a dynamic degree corrected stochastic block model (DCSBM) combined with statistical process monitoring to detect significant structural changes in evolving networks, exemplified by U.S. Senate voting patterns.

Contribution

It develops a novel dynamic network modeling and monitoring framework using DCSBM and statistical process control techniques for change detection.

Findings

Successfully detected periods of cohesion and polarization in Senate networks

Identified structural changes reflecting political shifts

Demonstrated effectiveness in local and global change detection

Abstract

In many applications it is of interest to identify anomalous behavior within a dynamic interacting system. Such anomalous interactions are reflected by structural changes in the network representation of the system. We propose and investigate the use of a dynamic version of the degree corrected stochastic block model (DCSBM) to model and monitor dynamic networks that undergo a significant structural change. We apply statistical process monitoring techniques to the estimated parameters of the DCSBM to identify significant structural changes in the network. Application of our surveillance strategy to the dynamic U.S. Senate co-voting network reveals that we are able to detect significant changes in the network that reflect both times of cohesion and times of polarization among Republican and Democratic party members. These findings provide valuable insight about the evolution of the bipartisan political system in the United States. Our analysis demonstrates that the dynamic DCSBM monitoring procedure effectively detects local and global structural changes in dynamic networks. The DCSBM approach is an example of a more general framework that combines parametric random graph models and statistical process monitoring techniques for network surveillance.