中国物理B ›› 2024, Vol. 33 ›› Issue (7): 70201-070201.doi: 10.1088/1674-1056/ad3b84
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Shun Ye(叶顺)1,2, Li-Lan Tu(涂俐兰)1,2,†, Xian-Jia Wang(王先甲)1,3, Jia Hu(胡佳)1,2, and Yi-Chao Wang(王薏潮)1,2
Shun Ye(叶顺)1,2, Li-Lan Tu(涂俐兰)1,2,†, Xian-Jia Wang(王先甲)1,3, Jia Hu(胡佳)1,2, and Yi-Chao Wang(王薏潮)1,2
摘要: In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions (i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method, two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions, some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows. (i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals. (ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network. (iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.
中图分类号: (Combinatorics; graph theory)