Dynamic Infinite Relational Model for Time-varying Relational Data Analysis

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, Joshua Tenenbaum


We propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks. Our proposed model abstracts observed time-varying object-object relationships into relationships between object clusters. We extend the infinite Hidden Markov model to follow dynamic and time-sensitive changes in the structure of the relational data and to estimate a number of clusters simultaneously. We show the usefulness of the model through experiments with synthetic and real-world data sets.