Dynamic Infinite Relational Model for Time-Varying Relational Data Analysis
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 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.
Cite
Text
Ishiguro et al. "Dynamic Infinite Relational Model for Time-Varying Relational Data Analysis." Neural Information Processing Systems, 2010.Markdown
[Ishiguro et al. "Dynamic Infinite Relational Model for Time-Varying Relational Data Analysis." Neural Information Processing Systems, 2010.](https://mlanthology.org/neurips/2010/ishiguro2010neurips-dynamic/)BibTeX
@inproceedings{ishiguro2010neurips-dynamic,
title = {{Dynamic Infinite Relational Model for Time-Varying Relational Data Analysis}},
author = {Ishiguro, Katsuhiko and Iwata, Tomoharu and Ueda, Naonori and Tenenbaum, Joshua B.},
booktitle = {Neural Information Processing Systems},
year = {2010},
pages = {919-927},
url = {https://mlanthology.org/neurips/2010/ishiguro2010neurips-dynamic/}
}