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

Abstract

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