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A Mean-Field Variational Bayesian Approach to Detecting Overlapping Communities with Inner Roles Using Poisson Link Generation

  • Gianni CostaEmail author
  • Riccardo Ortale
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)

Abstract

A novel model-based machine-learning approach is presented for the unsupervised and exploratory analysis of node affiliations to overlapping communities with roles in networks. At the heart of our approach is a new Bayesian probabilistic generative model of directed networks, that treats roles as abstract behavioral classes explaining node linking behavior. A generalized weighted instance of directed affiliation modeling rules the strength of node participation in communities with whichever role through Gamma priors. Moreover, link establishment between nodes is governed by a Poisson distribution. The latter is parameterized so that, the stronger the affiliations of two nodes to common communities with respective roles, the more likely it is the formation of a connection. A coordinate-ascent algorithm is designed to implement mean-field variational inference for affiliation analysis and link prediction. A comparative experimentation on real-world networks demonstrates the superiority of our approach in community compactness, link prediction and scalability.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.ICAR-CNRRendeItaly

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