文献类型：期刊文献

英文题名：Opinion Dynamics on Higher-Order Social Networks: A Continuous-Time Perspective

作者：Duan, Zhaoyang[1];Yan, Jiangwei[1];Xue, Dong[1];Liu, Fangzhou[2];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150001, Peoples R China

年份：2026

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20262020709914);WOS:【SCI-EXPANDED(收录号:WOS:001760517500001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62173147 and Grant 62373123.

语种：英文

外文关键词：System-on-chip; Circuits and systems; Contacts; Feedback; Circuits; Application specific integrated circuits; Electronic mail; Complex networks; Message systems; Network topology; Continuous-time opinion dynamics; contraction analysis; higher-order interactions; weak ties

摘要：Opinion dynamics models that elucidate the evolution and formation of opinions conventionally focus on pairwise interactions within graphs, often overlooking the complex higher-order interactions that arise in real-world social networks, such as online meetings and group chats. In this article, a continuous-time dynamical system is developed to study opinion-forming processes over higher-order networks associated with undirected hypergraphs. The proposed model introduces a novel diffusion-like interaction function to characterize interactions of different orders over hypergraphs. The convergence and stability of the dynamical systems are further examined in both the presence and absence of stubborn individuals. Building on traditional opinion dynamics models and integrating weak-tie theory, we emphasize the critical role of higher-order interactions in shaping individual opinions, enhancing network communication efficiency, and mitigating opinion polarization. Finally, all theoretical results are extensively investigated and empirically validated through numerical experiments on both synthetic and real-world network datasets.