Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks

Autor:	Kobayashi, Tomoki, Eguchi, Koji
Rok vydání:	2013
Předmět:	Computer Science - Social and Information Networks Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition. Comment: NIPS 2013 Workshop on Frontiers of Network Analysis: Methods, Models, and Applications
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1312.2154 Zobrazit plný text záznamu View this record from Arxiv