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.
CITATION STYLE
Costa, G., & Ortale, R. (2016). A mean-field variational bayesian approach to detecting overlapping communities with inner roles using poisson link generation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9897 LNCS, pp. 110–122). Springer Verlag. https://doi.org/10.1007/978-3-319-46349-0_10
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