摘要：Identifying communities is an important problem in network analysis. Various approaches have been proposed in the literature, but most of them either rely on the topological structure of the network or the node attributes, with few integrating both aspects. Here we propose a community detection approach based on sp ectral c lustering combining information on both the network s tructure and node a ttributes (SpcSA). Some of the attributes may not describe the communities we are trying to detect correctly. These irrelevant attributes can add noise and lower the overall accuracy of community detection. To determine how much each attribute contributes to community detection, our method introduces a mechanism by which attribute weights can adjust themselves. We demonstrate the effectiveness of the proposed method through numerical simulation and with real-world data..
关键词：spectral clustering; community detection; stochastic block model; node attributes; normalized mutual information
