{"title":"CoRNI: A Co-Evolutionary Framework Integrating Reputation and Network Structure for Modeling Social Influence Dynamics","authors":"Hangjing Zhang;H. Vicky Zhao;Yixin Dai","doi":"10.1109/TSIPN.2025.3572295","DOIUrl":null,"url":null,"abstract":"The prevalence of Internet platforms, such as social media and web pages, enables users to share their opinions and observe each other's actions. Users may show strong advocacy and support for each other's opinions and decisions at one moment, while they may disagree with each other with polarized views later. These dynamic changes of supportive relationships pose challenges to influential entities such as government agencies, firms, politicians, experts and weblebrities, who aim to gain support from and have large influence on the public. To study this dynamics, we consider the Social Networks with Supportive Relationships (SN-SR), whose links represent actively supportive relationships and are disconnected when users disagree with each other. For these influential entities, their reputation and social influence impact each other's evolution over SN-SR, while few works study how to model this co-evolving pattern and how to analyze and predict the dynamics of their influence over networks. In this work, we use the network topology of the SN-SR as an intermediate variable to model and study the interplay between the reputation and influence, and propose an integrated framework called CoRNI to theoretically analyze its impact on social influence. We use simulations on synthetic networks and real Weibo data to validate our proposed model and theoretical analysis. This investigation provides important guidelines for influential entities to adjust their actions and improve their influence over the network.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"474-489"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11016283/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The prevalence of Internet platforms, such as social media and web pages, enables users to share their opinions and observe each other's actions. Users may show strong advocacy and support for each other's opinions and decisions at one moment, while they may disagree with each other with polarized views later. These dynamic changes of supportive relationships pose challenges to influential entities such as government agencies, firms, politicians, experts and weblebrities, who aim to gain support from and have large influence on the public. To study this dynamics, we consider the Social Networks with Supportive Relationships (SN-SR), whose links represent actively supportive relationships and are disconnected when users disagree with each other. For these influential entities, their reputation and social influence impact each other's evolution over SN-SR, while few works study how to model this co-evolving pattern and how to analyze and predict the dynamics of their influence over networks. In this work, we use the network topology of the SN-SR as an intermediate variable to model and study the interplay between the reputation and influence, and propose an integrated framework called CoRNI to theoretically analyze its impact on social influence. We use simulations on synthetic networks and real Weibo data to validate our proposed model and theoretical analysis. This investigation provides important guidelines for influential entities to adjust their actions and improve their influence over the network.
期刊介绍:
The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.