Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks
This work addresses the need for real-time estimation in dynamic social networks, offering an incremental improvement over existing offline methods.
The paper tackled the problem of online inference for evolving networks using Mixed Membership Stochastic Blockmodels, developing sequential Monte Carlo methods that outperformed baselines in prediction performance.
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.