A Hybrid Clique-Based Method with Structural Feature Node Extraction for Community Detection in Overlapping Networks
Sicheng Ma1, Lixiang Zhang2,*, Guocai Chen3, Zeyu Dai3, Junru Zhu4, Wei Fang1,*
1 School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
2 School of Continuing Education, Jiangnan University, Wuxi, China
3 China Ship Scientific Research Center, Wuxi, China
4 Amazon-Alexa Shopping, Seattle, WA, USA
* Corresponding Author: Lixiang Zhang. Email: 
; Wei Fang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073572
Received 21 September 2025; Accepted 29 December 2025; Published online 22 January 2026
Abstract
Community detection is a fundamental problem in network analysis for identifying densely connected node clusters, with successful applications in diverse fields like social networks, recommendation systems, biology, and cyberattack detection. Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously, which is a much more meaningful and challenging task. Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics. However, most of them focus on searching the entire solution space, which can be inefficient and lead to inadequate results. To overcome the problem, a structural feature node extraction method is first proposed that can effectively map a network into a structural embedding space. Thus, nodes within the network are classified into hierarchical levels based on their structural feature strength, and only nodes with relatively high strength are considered in subsequent search steps to reduce the search space. Then, a maximal-clique representation method is employed on the given vertex set to identify overlapping nodes. A hybrid clique-based multi-objective evolutionary algorithm with decomposition method is designed to address cliques and the remaining unexplored nodes separately. The number of communities generated with this allocation method is closer to the actual partition count with high division quality. Experimental results on nine usually used real-world networks, five synthetic networks, and two large-scale networks demonstrate the effectiveness of the proposed methodology in terms of community quality and algorithmic efficiency, compared to traditional, MOEA-based, and graph embedding-based community detection algorithms.
Keywords
Community detection; graph embedding; multi-objective evolutionary algorithm; cliques; link strength
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