{"title":"社交网络中的群体行为预测与演变","authors":"Jingchao Wang, Xinyi Zhang, Weimin Li, Xiao Yu, Fangfang Liu, Qun Jin","doi":"10.1109/mis.2024.3366668","DOIUrl":null,"url":null,"abstract":"Group behavior prediction and evolution in social networks aims to accurately predict and model trends and patterns of group behavior through detailed analysis of massive user data, which is of great significance to the formulation of marketing strategies, user experience, and business strategies. Therefore, experts in various fields are actively exploring the potential of social network data to develop more accurate group behavior prediction and evolution models. This article provides an overview of these studies and explores the challenges and opportunities faced by group behavior prediction and evolution in social networks.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"4 1","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Group Behavior Prediction and Evolution in Social Networks\",\"authors\":\"Jingchao Wang, Xinyi Zhang, Weimin Li, Xiao Yu, Fangfang Liu, Qun Jin\",\"doi\":\"10.1109/mis.2024.3366668\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Group behavior prediction and evolution in social networks aims to accurately predict and model trends and patterns of group behavior through detailed analysis of massive user data, which is of great significance to the formulation of marketing strategies, user experience, and business strategies. Therefore, experts in various fields are actively exploring the potential of social network data to develop more accurate group behavior prediction and evolution models. This article provides an overview of these studies and explores the challenges and opportunities faced by group behavior prediction and evolution in social networks.\",\"PeriodicalId\":13160,\"journal\":{\"name\":\"IEEE Intelligent Systems\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/mis.2024.3366668\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mis.2024.3366668","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Group Behavior Prediction and Evolution in Social Networks
Group behavior prediction and evolution in social networks aims to accurately predict and model trends and patterns of group behavior through detailed analysis of massive user data, which is of great significance to the formulation of marketing strategies, user experience, and business strategies. Therefore, experts in various fields are actively exploring the potential of social network data to develop more accurate group behavior prediction and evolution models. This article provides an overview of these studies and explores the challenges and opportunities faced by group behavior prediction and evolution in social networks.
期刊介绍：
IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications. Typically they are degreed professionals, with backgrounds in engineering, hard science, or business. The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future. Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems. Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies. Often issues are theme-based and collect articles around a contemporary topic under the auspices of a Guest Editor working with the EIC.
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