Discovering Communities in Networks: A Linear Programming Approach Using Max-Min Modularity

Community detection is a fundamental challenge in network science and graph theory that aims to reveal nodes’ structures. While most methods consider Modularity as a community quality measure, Max-Min Modularity improves the accuracy of the measure by penalizing the Modularity quantity when unrelated nodes are in the same community. In this paper, we propose a community detection approach based on linear programming using Max-Min Modularity. The experimental results show that our algorithm has a better performance than the previously known algorithms on some well-known instances.