Adaptive Overlapping Community Detection with Bayesian NonNegative Matrix Factorization

Xiaohua Shi, Hongtao Lu, Guanbo Jia

Abstract: Overlapping Community Detection from a real network is unsupervised, and it is hard to know the exact community number or quantized strength of every node related to each community. Using Non-negative Matrix Factorization (NMF) for Community Detection, we can find two non-negative matrices from whole network adjacent matrix, and the product of two matrices approximates the original matrix well. With Bayesian explanation in factorizing process, we can not only catch most appropriate count of communities in a large network with Shrinkage method, but also verify good threshold how a node should be assigned to a community in fuzzy situation.