Nonparametric Multi-group Membership Model for Dynamic Networks

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Myunghwan Kim, Jure Leskovec


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 underlying relations between 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 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. 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 our model achieves higher predictive performance on the future network forecasting and missing link prediction.