# Detecting Local Community Structures in Social Networks Using Concept Interestingness

## TL;DR

This paper introduces COIN, a novel community detection method in social networks that uses concept lattice construction and formal concept analysis to identify local community structures more accurately and efficiently.

## Contribution

The paper presents COIN, a new community detection approach leveraging concept interestingness and formal concept analysis, outperforming existing algorithms in accuracy and speed.

## Key findings

- COIN detects communities more accurately than Edge betweenness, Fast greedy modularity, and Infomap.
- COIN effectively extracts cliques and bridges in social networks.
- The method quickly identifies local community structures in real-world social networks.

## Abstract

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.