文献类型：期刊文献

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

作者：Li, Huxiong[1];Nasab, Samaneh Salehi[2];Roghani, Hamid[3];Roghani, Parya[4];Gheisari, Mehdi[1,7,8,9];Fernandez-Campusano, Christian[5];Abbasi, Aaqif Afzaal[6];Wu, Zongda[1]

机构：[1]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing, Zhejiang, Peoples R China;[2]Lorestan Univ, Dept Engn, Aleshtar Campus, Aleshtar, Iran;[3]Azarbaijan Shahid Madani Univ, Dept Comp Engn, Tabriz, Iran;[4]Islamic Azad Univ, Cent Tehran Branch, Dept Biol, Tehran, Iran;[5]Univ Santiago Chile USACH, Dept Elect Engn, Santiago 9170124, Chile;[6]Univ Palermo, Dept Earth & Marine Sci, Palermo, Italy;[7]Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Comp Sci & Engn, Chennai 602105, Tamilnadu, India;[8]Islamic Azad Univ, Dept Comp Sci, Tehran, Iran;[9]Shenzhen BKD Co LTD, Dept R&D, Shenzhen, Peoples R China

年份：2024

卷号：185

外文期刊名：CHAOS SOLITONS & FRACTALS

收录：SCI-EXPANDED(收录号：WOS:001257839100001)、、EI(收录号：20242516263595)、Scopus(收录号：2-s2.0-85195873183)、WOS

基金：Acknowledgement This work was supported by National Natural Science Foundation of China (Grant No. 62341208) , Natural Science Foundation of Zhejiang Province (Grant Nos. LY23F020006 and LR23F020001) . Moreover, this has been supported by Islamic Azad Unviersity, Iran with number 133713281361.

语种：英文

外文关键词：Community detection; Local similarity; Multi -factor scoring; Label selection

外文摘要：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.