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Dynamic infinite relational model for time-varying relational data analysis

Author(s)
Ishiguro, Katsuhiko; Iwata, Tomoharu; Ueda, Naonori; Tenenbaum, Joshua B
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Abstract
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 timevarying 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.
Date issued
2010
URI
http://hdl.handle.net/1721.1/112756
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Journal
Advances in Neural Information Processing Systems (NIPS)
Publisher
Neural Information Processing Systems Foundation
Citation
Ishiguro, Katsuhiko et al. "Dynamic infinite relational model for time-varying relational data analysis." Advances in Neural Information Processing Systems (NIPS 2010), December 6-10 2010, Vancouver, Canada, Neural Information Processing Systems Foundation, 2010 © 2010 Neural Information Processing Systems Foundation Inc
Version: Final published version
ISSN
1049-5258

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