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SCE: Subspace-based core expansion method for community detection in complex networks

Community detection is a way to understand the mesoscale characteristics of networked systems and has received much attention recently. Most existing community detection methods suffer from several problems including; weak stability due to employing a randomness factor, requiring the number of commu...

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Bibliographic Details
Published in:Physica A 2019-08, Vol.527, p.121084, Article 121084
Main Authors: Mohammadi, Mehrnoush, Moradi, Parham, Jalili, Mahdi
Format: Article
Language:English
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Summary:Community detection is a way to understand the mesoscale characteristics of networked systems and has received much attention recently. Most existing community detection methods suffer from several problems including; weak stability due to employing a randomness factor, requiring the number of communities before starting the community identification process, and unable to recognize communities of various sizes. To overcome these challenges, in this paper a novel subspace-based core expansion method is proposed for identifying non-overlapping communities. The proposed method consists of three main steps. In the first step, the graph is mapped to a low dimensional space using a linear sparse coding method. The main idea behind the mapping strategy is that each data point within a combination of subspaces can be represented as a linear combination of other points. In the second step, a novel node ranking strategy is developed to calculate the goodness of nodes to be considered in identifying community cores. Finally, a novel label propagation mechanism is proposed to form final communities. Several experiments are performed to evaluate the effectiveness of the proposed method on real and synthetic networks. Obtained results reveal the better performance of the proposed method compared to some baseline and state-of-the-art community detection methods. •A novel subspace-based community detection method called SCE is proposed.•SCE maps the graph into a low dimensional subspace using linear sparse coding.•A novel node ranking strategy is proposed to identify community cores.•A novel label propagation strategy is used to form communities around identified cores.•The results of experiments reveal the effectiveness of the proposed method.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2019.121084