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Efficient detection of communities with significant overlaps in networks: Partial community merger algorithm

Published online by Cambridge University Press:  20 November 2017

ELVIS H. W. XU  and
PAK MING HUI
ELVIS H. W. XU
Affiliation:
Department of Physics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China (e-mail: hwxu@phy.cuhk.edu.hk, pmhui@phy.cuhk.edu.hk)
PAK MING HUI
Affiliation:
Department of Physics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China (e-mail: hwxu@phy.cuhk.edu.hk, pmhui@phy.cuhk.edu.hk)
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Abstract

Detecting communities in large-scale social networks is a challenging task where each vertex may belong to multiple communities. Such behavior of vertices and the implied strong overlaps among communities render many detection algorithms invalid. We develop a Partial Community Merger Algorithm (PCMA) for detecting communities with significant overlaps as well as slightly overlapping and disjoint ones. It is a bottom-up approach based on properly reassembling partial information of communities revealed in ego networks of vertices to reconstruct complete communities. We propose a novel similarity measure of communities and an efficient merger process to address the two key issues—noise control and merger order—in implementing this approach. PCMA is tested against two benchmarks and overall it outperforms all compared algorithms in both accuracy and efficiency. It is applied to two huge online social networks, Friendster and Sina Weibo. Millions of communities are detected and they are of higher qualities than the corresponding metadata groups. We find that the latter should not be regarded as the ground-truth of structural communities. The significant overlapping pattern found in the detected communities confirms the need of new algorithms, such as PCMA, to handle multiple memberships of vertices in social networks.

Keywords

community detectioncommunity structureoverlapping communitypartial communitysocial networksvertex membershipsmetadata groupslinear complexity

Information

Type
Research Article
Information
Network Science , Volume 6 , Issue 1 , March 2018 , pp. 71 - 96
Copyright
Copyright © Cambridge University Press 2017 

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