Smart flattening cuckoo search algorithm for community detection in multiplex networks

• Published in: Social Network Analysis and Mining Vol. 15; no. 1; p. 39
• Main Authors: Boukabene, Randa, Benbouzid-Si Tayeb, Fatima, Dakiche, Narimene
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 13.04.2025; Springer Nature B.V
• Subjects: Algorithms; Applications of Graph Theory and Complex Networks; Clustering; Community; Computer Science; Correlation coefficients; Data Mining and Knowledge Discovery; Economics; Flattening; Game Theory; Humanities; Law; Methodology of the Social Sciences; Modularity; Multiplexing; Nodes; Original Article; Search algorithms; Social and Behav. Sciences; Social networks; Statistics for Social Sciences; Network flattening; Deepwalk; Cuckoo search optimization; Community detection; Multiplex networks; Node2vec; Pearson correlation coefficient
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-025-01430-1

Community detection in social networks is a promising research area. However, network exploration with multiple types of relationships remains an underexplored yet crucial domain. To address the challenge of community detection in multiplex networks, we propose SFCSA, a smart flattening-based Cuckoo Search Algorithm, designed to address the complexities of such networks effectively. SFCSA unfolds in three integral steps. Firstly, we employ DeepWalk and node2vec algorithms to extract node embeddings, which are merged to construct the multiplex network node embeddings, minimizing information loss. Secondly, a flattened network is built by merging the network layers and adopting the Pearson correlation coefficient as a weight to capture node similarity. Thirdly, we employ a community detection approach that unveils the community structure through a cuckoo search algorithm applied to the multiplex node embeddings to extract the best set of cluster centres by maximizing the modularity function. Experimental evaluations across synthetic and real-world multiplex networks attest to the efficiency and effectiveness of our approach, outperforming state-of-the-art algorithms.