Fairness-Aware Maximal Cliques Identification in Attributed Social Networks With Concept-Cognitive Learning

Abstract

Attributed social networks are pervasive in real life and play a crucial role in shaping various aspects of society. These networks not only capture the connections between individuals but also encompass the associated attributes and characteristics. Analyzing and understanding these attributes provide insights into social behaviors, information diffusion patterns, and the formation of influential communities. Consequently, we propose a novel algorithm for detecting fairness-aware maximal cliques in the attributed social networks. We extract the concept lattice of attributed social networks and quantify these concepts using the concept stability and fairness measures defined in this article. By utilizing the proposed fairness-aware distance, we identify fairness-aware maximal cliques within attributed social networks. The effectiveness of the algorithm is then validated using five real-world network datasets. Experimental results fully demonstrate the effectiveness and scalability of our approach in identifying key structures, analyzing attribute networks, and promoting the development of responsible computational systems.