Spectral clustering-based network community detection with node attributes

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 spectral clustering combining information on both the network structure and node attributes (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.

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