Statistics and Its Interface

Volume 12 (2019)

Number 1

Spectral clustering-based network community detection with node attributes

Pages: 123 – 133

DOI: http://dx.doi.org/10.4310/SII.2019.v12.n1.a11

Authors

Fengqin Tang (School of Mathematics and Statistics, Lanzhou University, Lanzhou, China; and School of Mathematical Sciences, Huaibei Normal University, Huaibei, China)

Yuanyuan Wang (School of Mathematics and Statistics, Lanzhou University, Lanzhou, China)

Jinxia Su (School of Mathematics and Statistics, Lanzhou University, Lanzhou, China)

Chunning Wang (School of Mathematics and Statistics, Lanzhou University, Lanzhou, China)

Abstract

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.

Keywords

spectral clustering, community detection, stochastic block model, node attributes, normalized mutual information

2010 Mathematics Subject Classification

Primary 62-07. Secondary 68U20, 91D30.

Full Text (PDF format)

The project was sponsored by the National Natural Science Foundation of China (11301236), the Natural Science Foundation of the AnHui Higher Education Institutions of China (KJ2017A377, KJ2017A376), and the Anhui Provincial Natural Science Foundation (1608085QG169).

Received 13 November 2017

Published 26 October 2018