Diversified top-k maximal clique detection in Social Internet of Things

Abstract Social Internet of Things (SIoT), an IoT where things are autonomously capable of establishing relationships with other smart objects related to humans, allows them to interact within a social structure based on relationships. Importantly, exploiting the social structures of smart objects in SIoT is important for supervision and management of various services. Diversified top- k maximal clique, as a novel social structure, can be used for anomaly detection, and smart community detection from SIoT. However, the scalability of the existing approaches for detecting diversified top- k maximal cliques is becoming a significant challenge faced in the big graph. To this end, this paper proposes a novel diversified top- k maximal clique detection approach based on formal concept analysis. Specifically, we firstly prove the existence of equivalence relation between maximal cliques and equiconcepts which are a class of special concepts where the extent and intent are the same. Based on this equivalence relation, an efficient and innovative approach based on formal concept analysis for identifying diversified top- k maximal cliques is then further presented. Finally, three real-world social network datasets are adopted in experiments for the validation of effectiveness of our approach in SIoT.