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CEO: Identifying Overlapping Communities via Construction, Expansion and Optimization

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Overlapping communities are ubiquitous in real-world systems. For overlapping community detection, local expansion methods excel in scalability and efficiency yet have poor tolerance to low-quality seeds and communities. Based on our previous work, we introduce a more robust local-expansion-based overlapping community detection algorithm, named CEO, performing Construction, Expansion and Optimization sub-processes. To solve the poor fault tolerance problem, CEO discards low-quality seeds and communities in each sub-process based on optimizing node memberships. CEO was compared to thirteen noted algorithms by examining the performance on five groups of artificial networks and sixteen real-world networks with ground-truth communities. Experimental results showed CEO performs the best in identifying overlapping communities, which verifies the effectiveness of discarding low-quality seeds and communities in solving the poor fault tolerance problem.(c) 2022 Elsevier Inc. All rights reserved.

Overlapping community detectionLocal expansionPoor tolerance problemDiscarding low-quality seeds andcommunitiesNode membershipsCOMPLEX NETWORKSSOCIAL NETWORKSALGORITHMSIMILARITY

Ding, Xiaoyu、Yang, Hailu、Zhang, Jianpei、Yang, Jing、Xiang, Xiaohong

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Chongqing Univ Posts & Telecommun

Harbin Univ Sci & Technol

Harbin Engn Univ

2022

Information Sciences

Information Sciences

EISCI
ISSN:0020-0255
年,卷(期):2022.596
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