Nonparametric Multi-group Membership Model for Dynamic Networks
Kim, Myunghwan, Leskovec, Jure
–Neural Information Processing Systems
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.
Neural Information Processing Systems
Feb-14-2020, 16:58:02 GMT
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