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  5. Scalable Spectral Clustering for Overlapping Community Detection in Large-Scale Networks

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Article
English
2019

Scalable Spectral Clustering for Overlapping Community Detection in Large-Scale Networks

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English
2019
IEEE Transactions on Knowledge and Data Engineering
Vol 32 (4)
DOI: 10.1109/tkde.2019.2892096

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Guanrong Chen
Guanrong Chen

City University Of Hong Kong

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Hadrien Van Lierde
Tommy W. S. Chow
Guanrong Chen

Abstract

While the majority of methods for community detection produce disjoint communities of nodes, most real-world networks naturally involve overlapping communities. In this paper, a scalable method for the detection of overlapping communities in large networks is proposed. The method is based on an extension of the notion of normalized cut to cope with overlapping communities. A spectral clustering algorithm is formulated to solve the related cut minimization problem. When available, the algorithm may take into account prior information about the likelihood for each node to belong to several communities. This information can either be extracted from the available metadata or from node centrality measures. We also introduce a hierarchical version of the algorithm to automatically detect the number of communities. In addition, a new benchmark model extending the stochastic blockmodel for graphs with overlapping communities is formulated. Our experiments show that the proposed spectral method outperforms the state-of-the-art algorithms in terms of computational complexity and accuracy on our benchmark graph model and on five real-world networks, including a lexical network and large-scale social networks. The scalability of the proposed algorithm is also demonstrated on large synthetic graphs with millions of nodes and edges.

How to cite this publication

Hadrien Van Lierde, Tommy W. S. Chow, Guanrong Chen (2019). Scalable Spectral Clustering for Overlapping Community Detection in Large-Scale Networks. IEEE Transactions on Knowledge and Data Engineering, 32(4), pp. 754-767, DOI: 10.1109/tkde.2019.2892096.

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Publication Details

Type

Article

Year

2019

Authors

3

Datasets

0

Total Files

0

Language

English

Journal

IEEE Transactions on Knowledge and Data Engineering

DOI

10.1109/tkde.2019.2892096

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