Opinion consensus incorporating higher-order interactions in individual-collective networks

doi:10.1088/1674-1056/ad3b84

中国物理B ›› 2024, Vol. 33 ›› Issue (7): 70201-070201.doi: 10.1088/1674-1056/ad3b84

• • 下一篇

Opinion consensus incorporating higher-order interactions in individual-collective networks

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

1 Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430065, China;
2 College of Science, Wuhan University of Science and Technology, Wuhan 430065, China;
3 Economics and Management School of Wuhan University, Wuhan University, Wuhan 430065, China

收稿日期:2024-01-20 修回日期:2024-03-03 接受日期:2024-04-07 出版日期:2024-06-18 发布日期:2024-06-28
通讯作者: Li-Lan Tu E-mail:tulilan@wust.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 72031009 and 61473338).

Opinion consensus incorporating higher-order interactions in individual-collective networks

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

1 Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430065, China;
2 College of Science, Wuhan University of Science and Technology, Wuhan 430065, China;
3 Economics and Management School of Wuhan University, Wuhan University, Wuhan 430065, China

Received:2024-01-20 Revised:2024-03-03 Accepted:2024-04-07 Online:2024-06-18 Published:2024-06-28
Contact: Li-Lan Tu E-mail:tulilan@wust.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 72031009 and 61473338).

摘要/Abstract

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.

关键词: two-layer social networks, individual and collective opinions, higher-order interactions, consensus, Lyapunov's first method

Abstract: 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.

Key words: two-layer social networks, individual and collective opinions, higher-order interactions, consensus, Lyapunov's first method

中图分类号: (Combinatorics; graph theory)

02.10.Ox

02.30.Hq (Ordinary differential equations) 89.65.Ef (Social organizations; anthropology ?) 89.70.-a (Information and communication theory)

Shun Ye(叶顺), Li-Lan Tu(涂俐兰), Xian-Jia Wang(王先甲), Jia Hu(胡佳), and Yi-Chao Wang(王薏潮). Opinion consensus incorporating higher-order interactions in individual-collective networks[J]. 中国物理B, 2024, 33(7): 70201-070201.

参考文献

[1] Sîrbu A, Loreto V, Servedio V D P and Tria F 2016 Participatory Sensing, Opinions and Collective Awareness (Berlin: Springer) pp. 363-401
[2] Rowland F 2021 Portal-Libr. Acad. 11 1009
[3] Dong Y C, Ding Z G, Martínez L and Herrera F 2017 Inf. Sci. 397 187
[4] Baumann F, Lorenz-Spreen P, Sokolov I M and Starnini M 2020 Phys. Rev. Lett. 124 048301
[5] Dong Y C, Zhan M, Kou G, Ding Z G and Liang H M 2018 Inf. Fus. 43 57
[6] Gonzalez M C, Sousa A O and Herrmann H J 2004 Int. J. Mod. Phys. C 15 45
[7] Pires M A, Oestereich A L and Crokidakis N 2017 J. Stat. Mech-theory E 2018 053407
[8] Varma V S, Morǎrescu I C, Lasaulce S and Martin S 2018 IEEE Contr. Syst. Lett. 2 593
[9] Urena R, Kou G, Dong Y C, Chiclana F and Herrera-Viedma E 2019 Inf. Fus. 478 461
[10] Cheng C, Luo Y, Yu C B and Ding W P 2022 Chin. Phys. B 31 018701
[11] French J R 1956 Psychol. Rev. 63 181
[12] Ising, E 1925 Zeitschr. f. Phys. 31 253
[13] Clifford P and Sudbury A 1973 Biometrika 60 581
[14] Sznajd-Weron K and Sznajd J 2000 Int. J. Mod. Phys. C 11 1157
[15] DeGroot M H 1974 J. Am. Stat. Assoc. 69 118
[16] Friedkin N E and Johnsen E C 1990 J. Math. Sociol. 15 193
[17] Deffuant G, Neau D, Amblard F and Weisbuch G 2000 Adv. Complex. Syst. 3 87
[18] Rainer H and Krause U 2002 J. Artif. Soc. Soc. Simul. 5 1
[19] Altafini C 2012 IEEE Trans. Automat. Control. 58 935
[20] Jiao Y and Li Y 2021 Inf. Fus. 65 128
[21] Moldovan S, Muller E, Richter Y and Yom-Tov E 2017 Int. J. Res. Mark 34 536
[22] Cheng C and Yu C 2019 Physica A 532 121900
[23] Watts D J and Strogatz S H 1998 Nature 393 440
[24] Barabási A L and Albert R 1999 Science 286 509
[25] Tian Z, Dong G, Du R and Ma J 2016 Physica A 450 601
[26] Wang Y M, Guo T Y, Li W D and Chen Bo 2020 Chin. Phys. B 29 100204
[27] Kang R and Li X 2022 Automatica (Oxf) 137 110138
[28] Proskurnikov A V, Matveev A S, Cao M, Matveev and Alexey S 2015 IEEE Trans. Automat. Control. 61 1524
[29] Vasca F, Bernardo C and Iervolino R 2021 Automatica (Oxf) 129 109683
[30] Lanchier N and Li H L 2022 J. Stat. Phys. 187 20
[31] Jia P, MirTabatabaei A, Friedkin N E and Bullo F 2015 SIAM. Rev. 57 369
[32] Ruf S F, Paarporn K, Pare P E and Egerstedt M 2017 IEEE 56th annual conference on decision and control, December 12-15, 2017, Melbourne, Australia, p. 2935
[33] Ruf S F, Paarporn K and Pare P E 2019 IEEE Trans. Netw. Sci. Eng. 7 1764
[34] Battiston, F, Amico E, Barrat A, Bianconi G, Arruda G F, Franceschiello B, Iacopini I, Kefi S, Latora V, Moreno Y, Murray M M, Peixoto T P, Vaccarino F and Petri G 2021 Nat. Phys 17 1093
[35] Wan J, Ichinose G, Small M, Sayama H, Moreno Y and Cheng C Q 2022 Chaos, Solitons and Fractals 164 112735
[36] Schawe H and Hernandez L 2022 Commun. Phys. 5 32
[37] Wang C 2022 Entropy 24 1300
[38] Kermack W O and McKendrick A G 1927 Proc. R. Soc. Lond 138 55
[39] Gehring T and Marx J 2023 Hist. Soc. Res. 48 7
[40] Vidyasagar M 2002 Nonlinear systems analysis (Philadelphia: Society for Industrial and Applied Mathematics) pp. 57-87

Opinion consensus incorporating higher-order interactions in individual-collective networks

Opinion consensus incorporating higher-order interactions in individual-collective networks

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