{"title":"Detection of misbehaving individuals in social networks using overlapping communities and machine learning","authors":"Wejdan Alshlahy , Delel Rhouma","doi":"10.1016/j.jksuci.2024.102110","DOIUrl":null,"url":null,"abstract":"<div><p>Detecting misbehavior in social networks is essential for maintaining trust and reliability in online communities. Traditional methods of identification often rely on individual attributes or structural network properties, which may overlook subtle or complex misbehavior patterns. This paper introduces a novel approach called OCMLMD that leverages network overlapping community structure and machine learning techniques to detect misbehavior. Our method combines graph-based analyses of network topology with state-of-theart machine learning algorithms to identify suspicious behavior indicative of misbehavior. Specifically, we target nodes that belong to multiple communities or exhibit weak connections within their community, utilizing a novel metric for selecting overlapping nodes. Additionally, we develop a machine learning model trained on relevant attributes extracted from social network data to detect misbehavior accurately. Extensive experiments on synthetic and real-world social network datasets demonstrate the superior performance of OCMLMD compared to baseline methods. Overall, our proposed approach offers a promising solution to the challenge of detecting misbehavior in social networks.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S131915782400199X/pdfft?md5=05f3b297c94f4bf78520d4c75716b31c&pid=1-s2.0-S131915782400199X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S131915782400199X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Detecting misbehavior in social networks is essential for maintaining trust and reliability in online communities. Traditional methods of identification often rely on individual attributes or structural network properties, which may overlook subtle or complex misbehavior patterns. This paper introduces a novel approach called OCMLMD that leverages network overlapping community structure and machine learning techniques to detect misbehavior. Our method combines graph-based analyses of network topology with state-of-theart machine learning algorithms to identify suspicious behavior indicative of misbehavior. Specifically, we target nodes that belong to multiple communities or exhibit weak connections within their community, utilizing a novel metric for selecting overlapping nodes. Additionally, we develop a machine learning model trained on relevant attributes extracted from social network data to detect misbehavior accurately. Extensive experiments on synthetic and real-world social network datasets demonstrate the superior performance of OCMLMD compared to baseline methods. Overall, our proposed approach offers a promising solution to the challenge of detecting misbehavior in social networks.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.