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Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

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


DOI: http://dx.doi.org/10.15439/2021F65

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 329335 ()

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Abstract. 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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