Dynamic infinite relational model for time-varying relational data analysis
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4071-dynamic-infinite-relational-model-for-time-varying-relational-data-analysis.pdf
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Author(s)
Ishiguro, Katsuhiko
Iwata, Tomoharu
Ueda, Naonori
Tenenbaum, Joshua B
Date Issued
2010
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/4071-dynamic-infinite-relational-model-for-time-varying-relational-data-analysis
