Modeling with Node Degree Preservation Can Accurately Find Communities

• DOI: 10.1609/aaai.v29i1.9201
• Publication Date: 2022-06-23T23:16:41Z
• Authors: Di Jin; Zheng Chen; Dongxiao He; Weixiong Zhang
• Keywords: Disjoint sets; Null model; Representation

An important problem in analyzing complex networks is discovery of modular or community structures embedded the networks. Although being promising for identifying network communities, popular stochastic models often do not preserve node degrees, thus reducing their representation power and applicability to real-world Here we address this critical problem. Instead using a blockmodel, adopted random-graph null model faithfully capture by preserving expected degrees. The new model, learned nonnegative matrix factorization, more accurate robust representing than existing methods. Our results from extensive experiments on synthetic benchmarks show superior performance method over methods detecting both disjoint overlapping communities.