k-Cliques mining in dynamic social networks based on triadic formal concept analysis

Internet of Things (IoT), an emerging computing paradigm which interconnects various ubiquitous things is facilitating the advancement of computational intelligence. This paper aims at investigating the computation intelligence extraction approach with focus on the dynamic k-clique mining that is an important issue in social network analysis. The k-clique detection problem as one of the fundamental problems in computer science, can assist us to understand the organization style and behavioral patterns of users in social networks. However, real social networks usually evolve over time and it remains a challenge to efficiently detect the k-cliques from dynamic social networks. To address this challenge, this paper proposes an efficient k-clique dynamic detection theorem based on triadic formal concept analysis (TFCA) with completed mathematical proof. With this proposed detection theorem, we prove that the k-cliques detection problem is equivalent to finding the explicit k-cliques generated from k-triadic equiconcepts plus the implicit k-cliques derived from its high-order triadic equiconcepts. Theoretical analysis and experimental results illustrate that the proposed detection algorithm is efficient for finding the k-cliques and exploring the dynamic characteristics of the sub-structures in social networks.