PROJECT TITLE :

Stochastic Block modeling and Variational Bayes Learning for Signed Network Analysis - 2017

ABSTRACT:

Signed networks with positive and negative links attract considerable interest in their finding out since they contain more data than unsigned networks. Community detection and sign (or angle) prediction are still primary challenges, as the basic issues of signed network analysis. For this, a generative Bayesian approach is presented wherein 1) a signed stochastic blockmodel is proposed to characterize the community structure within the context of signed networks, by explicit formulating the distributions of the density and frustration of signed links from a stochastic perspective, and 2) a model learning algorithm is advanced by theoretical deriving a variational Bayes EM for the parameter estimation and variation-based mostly approximate evidence for the model selection. The comparison of the on top of approach with the state-of-the-art strategies on synthetic and real-world networks, shows its advantage within the community detection and sign prediction for the exploratory networks.


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