An Empirical Study of Absolute-Fairness Maximal Balanced Cliques Detection Based on Signed Attribute Social Networks: Considering Fairness and Balance


Yixuan Yang, Sony Peng, Doo-Soon Park, Hye-Jung Lee, Phonexay Vilakone, Journal of Information Processing Systems Vol. 20, No. 2, pp. 200-214, Apr. 2024  

https://doi.org/10.3745/JIPS.04.0306
Keywords: Absolute-Fairness Maximal Balanced Cliques, Attributed Social Network, Fairness of Nodes, Signed Social Network
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Abstract

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the “information cocoon” bottleneck, and reduce the occurrence of “group polarization” in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.


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Cite this article
[APA Style]
Yang, Y., Peng, S., Park, D., Lee, H., & Vilakone, P. (2024). An Empirical Study of Absolute-Fairness Maximal Balanced Cliques Detection Based on Signed Attribute Social Networks: Considering Fairness and Balance. Journal of Information Processing Systems, 20(2), 200-214. DOI: 10.3745/JIPS.04.0306.

[IEEE Style]
Y. Yang, S. Peng, D. Park, H. Lee, P. Vilakone, "An Empirical Study of Absolute-Fairness Maximal Balanced Cliques Detection Based on Signed Attribute Social Networks: Considering Fairness and Balance," Journal of Information Processing Systems, vol. 20, no. 2, pp. 200-214, 2024. DOI: 10.3745/JIPS.04.0306.

[ACM Style]
Yixuan Yang, Sony Peng, Doo-Soon Park, Hye-Jung Lee, and Phonexay Vilakone. 2024. An Empirical Study of Absolute-Fairness Maximal Balanced Cliques Detection Based on Signed Attribute Social Networks: Considering Fairness and Balance. Journal of Information Processing Systems, 20, 2, (2024), 200-214. DOI: 10.3745/JIPS.04.0306.