Abstract
Detecting network overlapping community has become a very hot research topic in the literature. However, overlapping community detection for count-value networks that naturally arise and are pervasive in our modern life, has not yet been thoroughly studied. We propose a generative model for count-value networks with overlapping community structure and use the Indian buffet process to model the community assignment matrix Z; thus, provide a flexible nonparametric Bayesian scheme that can allow the number of communities K to increase as more and more data are encountered instead of to be fixed in advance. Both collapsed and uncollapsed Gibbs sampler for the generative model have been derived. We conduct extensive experiments on simulated network data and real network data, and estimate the inference quality on single variable parameters. We find that the proposed model and inference procedure can bring us the desired experimental results.
Highlights
Community detection is a fundamental problem in network analysis, as community structure which almost exists in all networks, is the most widely studied structural properties of networks
Existing network generative models can be grouped into two classes: the latent class model, and the latent feature model
We propose a generative model for count-value networks with overlapping community structure: the network is modeled as a Poisson point process, after applying Poisson factor analysis on the corresponding count matrix, we obtain M = Z ZT, which is akin to the mixed membership stochastic block model (MMSB) [5] that can express the overlapping community structure
Summary
Community detection is a fundamental problem in network analysis, as community structure which almost exists in all networks, is the most widely studied structural properties of networks. The IBP is used as the prior to model the community assignment matrix Z; allows the number of communities K to be determined at inference time instead of to be predefined. Both a collapsed and an uncollapsed Gibbs sampler for the generative model have been derived. Karrer and Newman introduced the DCSBM model [9], they assumed that the links between nodes i and j follow a Poisson distribution and, represented network as a count adjacent matrix.
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