Structure Balance and Opinions Dynamic in Signed Social Network

LI Zhenpeng, TANG Xijin

系统科学与复杂性(英文) ›› 2023, Vol. 36 ›› Issue (4) : 1626-1640.

PDF(417 KB)
中国科学院数学与系统科学研究院期刊网
JSSC
PDF(417 KB)
系统科学与复杂性(英文) ›› 2023, Vol. 36 ›› Issue (4) : 1626-1640. DOI: 10.1007/s11424-023-1482-8

Structure Balance and Opinions Dynamic in Signed Social Network

    LI Zhenpeng1, TANG Xijin2,3
作者信息 +

Structure Balance and Opinions Dynamic in Signed Social Network

    LI Zhenpeng1, TANG Xijin2,3
Author information +
文章历史 +

摘要

In this paper, the authors consider both the nodes’ opinions dynamics and signed network edges’ evolution. Simulated Annealing Algorithm is applied for searching the minimal global energy function, and bounded confidence model is used for nodes’ opinions updating. The authors find that the local and global level of balance of signed network is consistent with collective opinions 2-polarization. This property is explainable in terms of the structure balance of the sign distributions on the nodes and edges. The level of balance and the final opinions polarization pattern are achieved depends on the initial density of signed network, and the percentage of initial positive edges. Numerical simulations of the proposed model display a rich and intuitive behavior of the opinions polarization processes. In particular, the authors show that opinions persistent fluctuations is consistent with minimal global the energy function. This work verify that signed social networks are indeed limited balanced, could be used to explain ubiquitous binary polarization phenomenon of real world.

Abstract

In this paper, the authors consider both the nodes’ opinions dynamics and signed network edges’ evolution. Simulated Annealing Algorithm is applied for searching the minimal global energy function, and bounded confidence model is used for nodes’ opinions updating. The authors find that the local and global level of balance of signed network is consistent with collective opinions 2-polarization. This property is explainable in terms of the structure balance of the sign distributions on the nodes and edges. The level of balance and the final opinions polarization pattern are achieved depends on the initial density of signed network, and the percentage of initial positive edges. Numerical simulations of the proposed model display a rich and intuitive behavior of the opinions polarization processes. In particular, the authors show that opinions persistent fluctuations is consistent with minimal global the energy function. This work verify that signed social networks are indeed limited balanced, could be used to explain ubiquitous binary polarization phenomenon of real world.

关键词

Opinions dynamic / signed network / structure balance

Key words

Opinions dynamic / signed network / structure balance

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用
LI Zhenpeng , TANG Xijin. Structure Balance and Opinions Dynamic in Signed Social Network. 系统科学与复杂性(英文), 2023, 36(4): 1626-1640 https://doi.org/10.1007/s11424-023-1482-8
LI Zhenpeng , TANG Xijin. Structure Balance and Opinions Dynamic in Signed Social Network. Journal of Systems Science and Complexity, 2023, 36(4): 1626-1640 https://doi.org/10.1007/s11424-023-1482-8

参考文献

[1] Parisien C, Anderson C H, and Eliasmith C, Solving the problem of negative synaptic weights in cortical models, Neural Computation, 2008, 20(6): 1473-1494.
[2] Heider F, Attitudes and cognitive organization, The Journal of Psychology, 1946, 21(1): 107-112.
[3] Ghosn F, Palmer G, and Bremer S A, The mid 3 data set, 1993-2001: Procedures, coding rules, and description, Conflict Management and Peace Science, 2004, 21(2): 133-154.
[4] DuBois T, Golbeck J, and Srinivasan A, Predicting trust and distrust in social networks, 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, IEEE, 2011, 418-424.
[5] Larusso N, Bogdanov P, and Singh A, Identifying communities with coherent and opposing views, Proceeding of the 15th Annual Graduate Student Workshop in Computing, Santa Barbara: UCSB, 2010, 31-32.
[6] Hosseini-Pozveh M, Zamanifar K, and Naghsh-Nilchi A R, Assessing information diffusion models for influence maximization in signed social networks, Expert Systems with Applications, 2019, 119: 476-490.
[7] He Q, Sun L, Wang X, et al., Positive opinion maximization in signed social networks, Information Sciences, 2021, 558: 34-49.
[8] Li L, Fan Y, Zeng A, et al., Binary opinion dynamics on signed networks based on ising model, Physica A: Statistical Mechanics and Its Applications, 2019, 525: 433-442.
[9] Facchetti G, Iacono G, and Altafini C, Computing global structural balance in large-scale signed social networks, Proceedings of the National Academy of Sciences, 2011, 108(52): 20953-20958.
[10] Chen Y, Chen L, Sun X, et al., Coevolutionary dynamics of opinion propagation and social balance: The key role of small-worldness, The European Physical Journal B, 2014, 87(3): 1-5.
[11] Wu Y, Gao L, Zhang Y, et al., Structural balance and dynamics over signed ba scale-free network, Physica A: Statistical Mechanics and Its Applications, 2019, 525: 866-877.
[12] Bindel D, Kleinberg J, and Oren S, How bad is forming your own opinion? Games and Economic Behavior, 2015, 92: 248-265.
[13] Battiston F, Cairoli A, Nicosia V, et al., Interplay between consensus and coherence in a model of interacting opinions, Physica D: Nonlinear Phenomena, 2016, 323: 12-19.
[14] Shi G, Johansson M, and Johansson K H, How agreement and disagreement evolve over random dynamic networks, IEEE Journal on Selected Areas in Communications, 2013, 31(6): 1061-1071.
[15] Altafini C, Consensus problems on networks with antagonistic interactions, IEEE Transactions on Automatic Control, 2012, 58(4): 935-946.
[16] Shi G, Altafini C, and Baras J S, Dynamics over signed networks, SIAM Review, 2019, 61(2): 229-257.
[17] Altafini C and Lini G, Predictable dynamics of opinion forming for networks with antagonistic interactions, IEEE Transactions on Automatic Control, 2014, 60(2): 342-357.
[18] Cartwright D and Harary F, Structural balance: A generalization of heider’s theory, Psychological Review, 1956, 63(5): 277.
[19] Bail C A, Argyle L P, Brown T W, et al., Exposure to opposing views on social media can increase political polarization, Proceedings of the National Academy of Sciences, 2018, 115(37): 9216-9221.
[20] Conover M D, Ratkiewicz J, Francisco M, et al., Political polarization on Twitter, Proceedings of the 5th International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, 2011.
[21] Li Z P and Tang X J, From global polarization to local social mechanisms: A study based on abm and empirical data analysis, Studies in Computational Intelligence, 2015, 564: 29-40.
[22] Li Z P and Tang X J, Polarization and non-positive social influence: A hopfield model of emergent structure, International Journal of Knowledge and Systems Science, 2012, 3(3): 15-25.
[23] Easley D and Kleinberg J, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University, Cambridge, 2010.
[24] He Y X, Reinert G, Wang S C, et al., Sssnet: Semi-supervised signed network clustering, Proceedings of the 2022 SIAM International Conference on Data Mining (SDM’22), Alexandria, Virginia, USA, 2022.
[25] Derr T, Johnson C, Chang Y, et al., Balance in signed bipartite networks, Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), (Eds. by Zhu W W and Tao D C), Beijing, China, 2019.
[26] Cygan M, Pilipczuk M, Micha l Pilipczuk, et al., Sitting closer to friends than enemies, revisited, Theory of Computing Systems, 2015, 56(2): 394-405.
[27] Zheng X, Zeng D, and Wang F Y, Social balance in signed networks, Information Systems Frontiers, 2015, 17(5): 1077-1095.
[28] Harary F, On the measurement of structural balance, Systems Research and Behavioral Science, 1959, 4(4): 316-323.
[29] Wasserman S and Faust K, Social Network Analysis: Methods and Applications, Structural Analysis in the Social Sciences, Cambridge University, Cambridge, 1994.
[30] Antal T, Krapivsky P L and Redner S, Social balance on networks: The dynamics of friendship and enmity, Physica D: Nonlinear Phenomena, 2006, 224(1): 130-136.
[31] Henley N M, Horsfall R B, and Soto C D, Goodness of figure and social structure, Psychological Review, 1969, 76(2): 194-204.
[32] Terzi E and Winkler M, A spectral algorithm for computing social balance, Algorithms and Models for the Web Graph, Eds. by Frieze A, Horn P, and Pralat P, Springer, Berlin, 2011.
[33] Davis J A, Clustering and structural balance in graphs, Social Networks, 1977, 20(2): 27-33.
[34] Harary F, On local balance and N-balance in signed graphs, Michigan Mathematical Journal, 1955, (3): 37-41.
[35] Doreian P and Mrvar A, A partitioning approach to structural balance, Social Networks, 1996, (18): 149-168.
[36] Wu L, Ying X, Wu X, et al., Spectral analysis of k-balanced signed graphs, Advances in Knowledge Discovery and Data Mining, 2011, 6635: 1-12.
[37] Kim M and Candan K S, Sbv-cut: Vertex-cut based graph partitioning using structural balance vertices, Data and Knowledge Engineering, 2012, 72: 285-303.
[38] Feige U, Hajiaghayi M T, and Lee J R, Improved approximation algorithms for minimum weight vertex separators, SIAM Journal on Computing, 2008, 38(2): 629-657.
[39] Leskovec J, Huttenlocher D, and Kleinberg J, Signed networks in social media, Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, Atlanta, Georgia, 2010.
[40] Liu H, Qu C, Niu Y, et al., The evolution of structural balance in time-varying signed networks, Future Generation Computer Systems, 2020, 102: 403-408.
[41] Mengersen K L and Tweedie R L, Rates of convergence of the hastings and metropolis algorithms, Annals of Statistics, 1996, 24(1): 101-121.
[42] Deffuant G, Neau D, Amblard F, et al., Mixing beliefs among interacting agents, Advances in Complex Systems, 2000, 3(1-4): 87-98.

基金

This research was supported by the National Natural Science Foundation of China under Grant No. 71661001.
PDF(417 KB)

117

Accesses

0

Citation

Detail
段落导航
相关文章

/

〈 〉