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.

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Track 3: Network Systems and Applications