A state-space mixed membership blockmodel for dynamic network tomography

TL;DR

This paper introduces a dynamic mixed membership blockmodel for analyzing evolving networks, capturing actors' changing roles over time, and demonstrates its effectiveness on social, email, and biological networks.

Contribution

It extends static mixed membership models with a state-space approach to model time-varying roles of actors in dynamic networks.

Findings

Revealed dynamic role patterns in social and biological networks.

Provided an efficient inference algorithm for the proposed model.

Successfully applied to real-world networks showing meaningful role changes.

Abstract

In a dynamic social or biological environment, the interactions between the actors can undergo large and systematic changes. In this paper we propose a model-based approach to analyze what we will refer to as the dynamic tomography of such time-evolving networks. Our approach offers an intuitive but powerful tool to infer the semantic underpinnings of each actor, such as its social roles or biological functions, underlying the observed network topologies. Our model builds on earlier work on a mixed membership stochastic blockmodel for static networks, and the state-space model for tracking object trajectory. It overcomes a major limitation of many current network inference techniques, which assume that each actor plays a unique and invariant role that accounts for all its interactions with other actors; instead, our method models the role of each actor as a time-evolving mixed membership vector that allows actors to behave differently over time and carry out different roles/functions when interacting with different peers, which is closer to reality. We present an efficient algorithm for approximate inference and learning using our model; and we applied our model to analyze a social network between monks (i.e., the Sampson's network), a dynamic email communication network between the Enron employees, and a rewiring gene interaction network of fruit fly collected during its full life cycle. In all cases, our model reveals interesting patterns of the dynamic roles of the actors.