{"title":"LeaDCD: Leadership concept-based method for community detection in social networks","authors":"","doi":"10.1016/j.ins.2024.121341","DOIUrl":null,"url":null,"abstract":"<div><p>Community discovery plays an essential role in analyzing and understanding the behavior and relationships of users in social networks. For this reason, various algorithms have been developed in the last decade for discovering the optimal community structure. In social networks, some individuals have special characteristics that make them well-known by others. These groups of users are called leaders and often have a significant impact on others, with an exceptional ability to build communities. In this paper, we propose an efficient method to detect communities in social networks using the concept of leadership (LeaDCD). The proposed algorithm mainly involves three phases. First, based on nodes' degree centrality and maximal cliques, some small groups of nodes (leaders) considered as seeds for communities are discovered. Next, unassigned nodes are added to the seeds through an expansion process to generate the initial community structure. Finally, small communities are merged to form the final community structure. To demonstrate the effectiveness of our proposal, we carried out comprehensive experiments on real-world and artificial graphs. The findings indicate that our algorithm outperforms other commonly used methods, demonstrating its high efficiency and reliability in discovering communities within social graphs.</p></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020025524012556","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"N/A","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Community discovery plays an essential role in analyzing and understanding the behavior and relationships of users in social networks. For this reason, various algorithms have been developed in the last decade for discovering the optimal community structure. In social networks, some individuals have special characteristics that make them well-known by others. These groups of users are called leaders and often have a significant impact on others, with an exceptional ability to build communities. In this paper, we propose an efficient method to detect communities in social networks using the concept of leadership (LeaDCD). The proposed algorithm mainly involves three phases. First, based on nodes' degree centrality and maximal cliques, some small groups of nodes (leaders) considered as seeds for communities are discovered. Next, unassigned nodes are added to the seeds through an expansion process to generate the initial community structure. Finally, small communities are merged to form the final community structure. To demonstrate the effectiveness of our proposal, we carried out comprehensive experiments on real-world and artificial graphs. The findings indicate that our algorithm outperforms other commonly used methods, demonstrating its high efficiency and reliability in discovering communities within social graphs.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.