Detecting Local Community Structures in Social Networks Using Concept Interestingness
This addresses the challenge of community detection in social network analysis, offering a method that leverages conceptual characteristics for improved accuracy, though it appears incremental as it builds on existing formal concept analysis techniques.
The paper tackles the problem of efficiently and accurately detecting local community structures in social networks by introducing COIN, a novel strategy that uses concept interestingness measures and formal concept analysis, achieving more accurate results than existing algorithms like Edge betweenness, Fast greedy modularity, and Infomap.
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.