Parameter Identification Method for Opinion Dynamics Models: Tested via Real Experiments

Nematollahzadeh, S.M.; Ozgoli, S.; Sayad Haghighi, M.

doi:10.22061/jecei.2020.6145.283

Document Type : Original Research Paper

Authors

¹ Iran Telecommunication Research Center, Tehran, Iran and Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

² Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

³ School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran and School of Electrical Engineering and Computer Science, Queensland University of Technology, Queensland , Australia

https://doi.org/10.22061/jecei.2020.6145.283

Abstract

Background and Objectives: One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks.
Methods: The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals’ opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then, the three models which are the most comprehensive and vastly accepted in the literature, are considered. These three models are then proven to satisfy the convexity condition, which means they can be used for the introduced method of identification.
Results: Four real experiments have been conducted in this research that their results verify the application of our method. The outcomes of these experiments are presented in this paper.
Conclusion:
Results show that the provide method is suited for parameter identification for opinion dynamic models.

Keywords

20.1001.1.23223952.2019.7.1.12.4

Main Subjects

Social Networks

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

Publisher’s Note

JECEI Publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher

Shahid Rajaee Teacher Training University

References

[1] A. Halu, “Dynamics of and on complex networks,” Ph.D. Dissertation, the College of Science of North-Eastern University, USA, 2014.

[2] S. Bolouki, R.P. Malham´e, M. Siami, N. Motee, “Éminence grise coalitions: On the shaping of public opinion,” IEEE Transactions on Control of Network Systems, 4(2): 133-145, 2015.

[3] T. Tatarenko, B. Touri, “Non-convex distributed optimization,” IEEE Transactions on Automatic Control, 62(8): 3744-3757, 2017.

[4] S. Shams Shamsabad Farahani, “Congestion control approaches applied to wireless sensor networks: A survey,” Journal of Electrical and Computer Engineering Innovations (JECEI), 6(2): 125-144, 2018.

[5] S. Minaee Jalil, A. khaleghi, “A CSA method for assigning client to servers in online social networks,” Journal of Electrical and Computer Engineering Innovations (JECEI), 3(2): 123-129, 2015.

[6] A. Mansouri, F. Taghiyareh, “Toward an emotional opinion formation model through agent-based modeling,” presented at the 2017 IEEE, 7^th International Conference on Computer and Knowledge Engineering, Mashhad, Iran, 2017.

[7] A. SeyedHassani, M. S. Haghighi, A. Khonsari, “Bayesian inference of private social network links using prior information and propagated data,” Journal of Parallel and Distributed Computing, 125: 72-80, 2018.

[8] Jr. French, “A formal theory of social power,” The Psychological Review, 63(3): 181-194, 1956.

[9] F. Harary, “A criterion for unanimity in French's theory of social power,” Studies in Social Power, Edited by D. Cartwright, Ann Arbor, MI: Institute for Social Research, 1959.

[10] M. H. DeGroot, “Reaching a consensus,” Journal of the American Statistical Association, 69(345): 118-121, 1974.

[11] R. P. ABELSON, Mathematical Models of the Distribution of Attitudes under Controversy. In N. Frederiksen & H. Gulliksen (Eds.), Contributions to Mathematical Psychology. New York: Holt, Rinehart & Winston, Inc. , 1964.

[12] E. Ising, “Report on the theory of ferromagnetism,” Zeitschrift fur physik, 31: 253-258, 1925.

[13] R. Axelrod, “The dissemination of culture: A model with local convergence and global polarization,” Journal of Conflict Resolution, 41(2): 203–226, 1997.

[14] N. E. Friedkin, “A structural theory of social influence,” 13. Cambridge University Press, 2006.

[15] N. E. Friedkin, E. C. Johnsen, “Social influence networks and opinion change,” Advances in Group Processes, 16(1): 1-29, 1999.

[16] N. E. Friedkin, E. C. Johnsen, “Social influence network theory: A sociological examination of small group dynamics,” 33, Cambridge University Press, 2011.

[17] F. Salimi Naneh Karan, S. Chakraborty, “Dynamics of a repulsive voter model,” IEEE Transactions on Computational Social Systems, 3(1): 13-22, 2016.

[18] D.-S. Lee, C. S. Chang, Y. Liu, “Consensus and polarization of binary opinions in structurally balanced networks,” IEEE Transaction on Computational Social Systems, 3(4): 141-150, 2016.

[19] M. J. Hendrickx, “A lifting approach to models of opinion dynamics with antagonisms,” presented at the 2014 IEEE, 53^rd Annual Conference on Decision and Control (CDC), Los Angeles, CA, USA, 2014.

[20] X. Chen Liu, T. Bas¸ar, M. A. Belabbas, “Exponential convergence of the discrete- and continuous-time Altafini models,” IEEE Transactions on Automatic Control, 62(12): 6168-6182, 2017.

[21] J. Liu, X. Chen, T. Başar, M. A. Belabbas, “Stability of discrete-time Altafini's model: A graphical approach,” presented at the 54^th IEEE Annual Conference on Decision and Control (CDC), Osaka, Japan, 2015.

[22] C. Altafini, F. Ceragioli, “Signed bounded confidence models for opinion dynamics,” Automatica, 98: 114-125, 2018.

[23] G. Deffuant, D. Neau, F.Amblard, and G. Weisbuch, “Mixing beliefs among interacting agents,” Advances in Complex Systems, 3: 87–98, 2000.

[24] R. Hegselmann, U. Krause, “Opinion dynamics and bounded confidence models, analysis, and simulation,” Journal of Artificial Societies and Social Simulation (JASSS), 5(3):2, 2002.

[25] J. Lorenz, “Continuous opinion dynamics under bounded confidence: A survey,” International Journal of Modern Physics C, 18(12): 1819–1838, 2007.

[26] F. Ceragioli, G. Lindmark, C. Veibäck, N. Wahlström, M. Lindfors, C. Altafini, “A bounded confidence model that preserves the signs of the opinions,” in Proc. 2016 European Control Conference: 543-548, 2016.

[27] Y. Dong, Z. Ding, H. Liang, F. Chiclana, “Asynchronous opinion dynamics with online and online interactions in bounded confidence model,” The Journal of Artificial Societies and Social Simulation (JASSS), 20(4): 1-12, 2017.

[28] A. V. Proskurnikov, R. Tempo, “A tutorial on modeling and analysis of dynamic social networks: Part I,” Annual Reviews in Control, 43: 65-79, 2017.

[29] A. V. Proskurnikov, R. Tempo, “A tutorial on modeling and analysis of dynamic social networks: Part II,” Annual Reviews in Control, 45: 166-190, 2018.

[30] S. M. Nematollahzadeh, S. Ozgoli, M. Sojoodi “Opinion influence network identification, using convex programming,” presented at the IEEE 8^th International Symposium on Telecommunications, Tehran, Iran, 2016.

[31] M. M. Zavlanos, S. P. Boyd, G. J. Pappas, “Genetic network identification using convex programming,” IET Systems Biology, 3(3): 155-166, 2009.

[32] M. M. Zavlanos, S. P. Boyd, G. J. Pappas, “Inferring stable genetic networks from steady-state data,” Automatica, 47(6): 1113-1122, 2011.

[33] M. Sattari, K. Zamanifar, “A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks,” Journal of Computational Science, 25: 122-133, 2018.

[34] V. Amelkin, F. Bullo, A. K. Singh, “Polar opinion dynamics in social networks,” IEEE Transactions on Automatic Control, 62(11): 5650-5665, 2017.

[35] L. Lei, C. Zhoub, J. Hea, J. Wanga, X. Lic, X. Wu, “Collective semantic behavior extraction in social networks,” Journal of Computational Science, 28: 236-244, 2018.

[36] F. R. Gantmacher, “The Theory of Matrices,” AMS Chelsea Publishing, 1987.

[37] S. Bolouki, P. M. Roland, “Linear consensus algorithms based on balanced asymmetric chains,” IEEE Transactions on Automatic Control, 60(10): 2808-2812, 2015.

[38] S. Bolouki, P. M. Roland “Consensus algorithms and the decomposition-separation theorem,” IEEE Transactions on Automatic Control, 61(9): 2357-2369, 2015.

[39] S. E. Parsegov, A. V. Proskurnikov, R. Tempo, N. E. Friedkin, “Novel multidimensional models of opinion dynamics in social networks,” IEEE Transactions on Automatic Control, 62(5): 2270-2285, 2017.

[40] S. E. Parsegov, A. V, Proskurnikov, R. Tempo, N. E. Friedkin “A new model of opinion dynamics for social actors with multiple interdependent attitudes and prejudices,” in Proc. IEEE 54^th Conference on Decision and Control (CDC), Osaka, Japan, 2015.

[41] S. Boyd, L. Vandenberghe, Convex optimization, Cambridge University Press, 2004.

[42] M. Grant, S. Boyd, CVX: Matlab Software for Disciplined Convex Programming, version 2.1, 2014.

[43] V. D. Blondel, A. Gajardo, M. Heymans, P. Senellart, . Van Dooren, “A measure of similarity between graph vertices: Applications to synonym extraction and web searching,” SIAM Review, 46(4): 647-666, 2004.

[44] http://sylicon.ir

LETTERS TO EDITOR

Journal of Electrical and Computer Engineering Innovations (JECEI) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in JECEI should be sent to the editorial office of JECEI within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Journal of Electrical and Computer Engineering Innovations (JECEI)

Parameter Identification Method for Opinion Dynamics Models: Tested via Real Experiments

References

References

Send comment about this article

Volume 7, Issue 1
January 2019
Pages 123-133

Parameter Identification Method for Opinion Dynamics Models: Tested via Real Experiments

References

References

Send comment about this article

Volume 7, Issue 1January 2019Pages 123-133

Volume 7, Issue 1
January 2019
Pages 123-133