Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks

Qirong Ho, Le Song, Eric Xing
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:342-350, 2011.

Abstract

Time-evolving networks are a natural presentation for dynamic social and biological interactions. While latent space models are gaining popularity in network modeling and analysis, previous works mostly ignore networks with temporal behavior and multi-modal actor roles. Furthermore, prior knowledge, such as division and grouping of social actors or biological specificity of molecular functions, has not been systematically exploited in network modeling. In this paper, we develop a network model featuring a state space mixture prior that tracks complex actor latent role changes through time. We provide a fast variational inference algorithm for learning our model, and validate it with simulations and held-out likelihood comparisons on real-world time-evolving networks. Finally, we demonstrate our model’s utility as a network analysis tool, by applying it to United States Congress voting data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-ho11b, title = {Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks}, author = {Ho, Qirong and Song, Le and Xing, Eric}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {342--350}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/ho11b/ho11b.pdf}, url = {https://proceedings.mlr.press/v15/ho11b.html}, abstract = {Time-evolving networks are a natural presentation for dynamic social and biological interactions. While latent space models are gaining popularity in network modeling and analysis, previous works mostly ignore networks with temporal behavior and multi-modal actor roles. Furthermore, prior knowledge, such as division and grouping of social actors or biological specificity of molecular functions, has not been systematically exploited in network modeling. In this paper, we develop a network model featuring a state space mixture prior that tracks complex actor latent role changes through time. We provide a fast variational inference algorithm for learning our model, and validate it with simulations and held-out likelihood comparisons on real-world time-evolving networks. Finally, we demonstrate our model’s utility as a network analysis tool, by applying it to United States Congress voting data.} }
Endnote
%0 Conference Paper %T Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks %A Qirong Ho %A Le Song %A Eric Xing %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-ho11b %I PMLR %P 342--350 %U https://proceedings.mlr.press/v15/ho11b.html %V 15 %X Time-evolving networks are a natural presentation for dynamic social and biological interactions. While latent space models are gaining popularity in network modeling and analysis, previous works mostly ignore networks with temporal behavior and multi-modal actor roles. Furthermore, prior knowledge, such as division and grouping of social actors or biological specificity of molecular functions, has not been systematically exploited in network modeling. In this paper, we develop a network model featuring a state space mixture prior that tracks complex actor latent role changes through time. We provide a fast variational inference algorithm for learning our model, and validate it with simulations and held-out likelihood comparisons on real-world time-evolving networks. Finally, we demonstrate our model’s utility as a network analysis tool, by applying it to United States Congress voting data.
RIS
TY - CPAPER TI - Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks AU - Qirong Ho AU - Le Song AU - Eric Xing BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-ho11b PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 342 EP - 350 L1 - http://proceedings.mlr.press/v15/ho11b/ho11b.pdf UR - https://proceedings.mlr.press/v15/ho11b.html AB - Time-evolving networks are a natural presentation for dynamic social and biological interactions. While latent space models are gaining popularity in network modeling and analysis, previous works mostly ignore networks with temporal behavior and multi-modal actor roles. Furthermore, prior knowledge, such as division and grouping of social actors or biological specificity of molecular functions, has not been systematically exploited in network modeling. In this paper, we develop a network model featuring a state space mixture prior that tracks complex actor latent role changes through time. We provide a fast variational inference algorithm for learning our model, and validate it with simulations and held-out likelihood comparisons on real-world time-evolving networks. Finally, we demonstrate our model’s utility as a network analysis tool, by applying it to United States Congress voting data. ER -
APA
Ho, Q., Song, L. & Xing, E.. (2011). Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:342-350 Available from https://proceedings.mlr.press/v15/ho11b.html.

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