Nonparametric Multi-group Membership Model for Dynamic Networks

TL;DR

This paper introduces a nonparametric multi-group membership model for dynamic networks that captures evolving group structures and improves prediction of future network states.

Contribution

It presents a novel model combining a distance dependent Indian Buffet Process, Factorial Hidden Markov models, and explicit group connectivity modeling for dynamic networks.

Findings

Enhanced predictive accuracy for future network states

Effective identification of evolving latent groups

Superior performance in link prediction tasks

Abstract

Relational data-like graphs, networks, and matrices-is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on the intuition that changes in the network structure are driven by the dynamics at the level of groups of nodes. We propose a nonparametric multi-group membership model for dynamic networks. Our model contains three main components: We model the birth and death of individual groups with respect to the dynamics of the network structure via a distance dependent Indian Buffet Process. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model. And, we explain the dynamics of the network structure by explicitly modeling the connectivity structure of groups. We demonstrate our model's capability of identifying the dynamics of latent groups in a number of different types of network data. Experimental results show that our model provides improved predictive performance over existing dynamic network models on future network forecasting and missing link prediction.