Abstract: A community detection approach (SCSA) based on the spectral clustering method that combines both structural information and node attributes information was proposed.Firstly,the SCSA algorithm converted the node-attributed graph to a weighted graph,where the edge weights are measured by attribute similarities.Then,the spectral clustering was applied on the weighted graph.The SCSA algorithm partitioned a network associated with attributes into K communities in which the nodes are not only well connected but also have similar attributes.Notice that not all attributes are useful in the clustering process,and irrelevant attributes can lower the overall accuracy of community detection by adding noise.To address this issue,an attribute weight self-adjustment mechanism embedded into spectral clustering was proposed in order to improve the community detection quality.Experiments demonstrate the effectiveness of the proposed algorithm.

Key words: community detection, spectral clustering, stochastic block model, normalized mutual information