  One key challenge in Social Network Analysis is to design an efficient and
accurate community detection procedure as a means to discover intrinsic
structures and extract relevant information. In this paper, we introduce a
novel strategy called (COIN), which exploits COncept INterestingness measures
to detect communities based on the concept lattice construction of the network.
Thus, unlike off-the-shelf community detection algorithms, COIN leverages
relevant conceptual characteristics inherited from Formal Concept Analysis to
discover substantial local structures. On the first stage of COIN, we extract
the formal concepts that capture all the cliques and bridges in the social
network. On the second stage, we use the stability index to remove noisy
bridges between communities and then percolate relevant adjacent cliques. Our
experiments on several real-world social networks show that COIN can quickly
detect communities more accurately than existing prominent algorithms such as
Edge betweenness, Fast greedy modularity, and Infomap.
