LMFLS: A new fast local multi-factor node scoring and label selection-based algorithm for community detection

• Published in: Chaos, solitons and fractals Vol. 185; p. 115126
• Main Authors: Li, Huxiong, Nasab, Samaneh Salehi, Roghani, Hamid, Roghani, Parya, Gheisari, Mehdi, Fernández-Campusano, Christian, Abbasi, Aaqif Afzaal, Wu, Zongda
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Community detection; Label selection; Local similarity; Multi-factor scoring; Local similarity; Multi-factor scoring; Community detection; Label selection
• ISSN: 0960-0779; 1873-2887
• DOI: 10.1016/j.chaos.2024.115126

Community detection is still regarded as one of the most applicable approaches for discovering latent information in complex networks. To meet the needs of processing large networks in today's world, it is important to propose fast methods that have low execution time and fast convergence speed, while maintaining algorithmic accuracy. To overcome these issues, a fast local multi-factor node scoring and label selection-based (LMFLS) method with low time complexity and fast convergence is proposed. Node scoring step incorporates diverse metrics to better assess impact of nodes from different aspects and obtain more meaningful order of nodes. In second step, to construct and stabilize initial structure of communities, an efficient label assignment technique based on the selection of the most similar neighbor is suggested. Moreover, two label selection strategies are proposed to significantly enhance the accuracy and improve convergence of the algorithm. During the label selection step, each node in graph tends to choose the most appropriate label based on a multi-criteria label influence from its surrounding nodes. Finally, by utilizing a novel merge method, small group of nodes are merged to form the final communities. Meanwhile, since drug repositioning is one of the popular research fields in therapeutics, to extend the application of the proposed algorithm in practical context, the LMFLS algorithm is applied on Drug-Drug network to find potential repositioning for drugs. Thorough experiments are conducted on both actual real-world networks and synthetic networks to assess the algorithm's performance and accuracy. The findings demonstrate that the proposed method outperforms state-of-the-art algorithms in terms of both accuracy and execution time. •A multi-factor criterion is used for computing nodes importance from diverse aspects.•Am efficient and fast strategy is used for label assignment to nodes.•A fast merge step is utilized to obtain more dense and accurate communities.•The LMFLS algorithm enhances accuracy, execution time, convergence speed, and RAM usage.•The result of LMFLS completely is robust and stable in comparison with other examined methods.