Group Behavior Prediction and Evolution in Social Networks

IF 5.6 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Intelligent Systems Pub Date : 2024-04-30 DOI:10.1109/mis.2024.3366668

Jingchao Wang, Xinyi Zhang, Weimin Li, Xiao Yu, Fangfang Liu, Qun Jin

引用次数: 0

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.

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社交网络中的群体行为预测与演变

社交网络中的群体行为预测与演化旨在通过对海量用户数据的详细分析，对群体行为的趋势和模式进行准确预测和建模，这对营销策略、用户体验和商业战略的制定具有重要意义。因此，各领域专家都在积极探索社交网络数据的潜力，以开发更准确的群体行为预测和演化模型。本文概述了这些研究，并探讨了社交网络中群体行为预测和演化所面临的挑战和机遇。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Intelligent Systems 工程技术-工程：电子与电气

CiteScore

13.80

自引率

3.10%

发文量

122

审稿时长

1 months

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.