Abstract
Recently, in many systems such as speech recognition and visual processing, deep learning has been widely implemented. In this research, we are exploring the possibility of using deep learning in community detection among the graph datasets. Graphs have gained growing traction in different fields, including social networks, information graphs, the recommender system, and also life sciences. In this paper, we propose a method of community detection clustering the nodes of various graph datasets. We cluster different category datasets that belong to affiliation networks, animal networks, human contact networks, human social networks, miscellaneous networks. The deep learning role in modeling the interaction between nodes in a network allows a revolution in the field of science relevant to graph network analysis. In this paper, we extend the gumbel softmax approach to graph network clustering. The experimental findings on specific graph datasets reveal that the new approach outperforms traditional clustering significantly, which strongly shows the efficacy of deep learning in graph community detection clustering. We do a series of experiments on our graph clustering algorithm, using various graph datasets: Zachary's karate club, Highland tribes, Train bombing, American Revolution, Dolphins, Zebra, Windsurfers, Les Misérables, Political books.
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The code for this research is available on github at: https://github.com/deepakacharyab/community_detection_gumbel_softmax
References
Acharya DB, Zhang H. Feature selection and extraction for graph neural networks. In: Proceedings of the 2020 ACM southeast conference, ACM SE ’20, page 252–255, New York, NY, USA. Association for Computing Machinery; 2020.
Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, Niculae V, Prettenhofer P, Gramfort A, Grobler J, Layton R, VanderPlas J, Joly A, Holt B, Varoquaux G. API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD workshop: languages for data mining and machine learning; 2013. p. 108–122.
Cuijuan W, Wenzhong T, Bo S, Jing F, Yanyang W. Review on community detection algorithms in social networks. In: 2015 IEEE International conference on progress in informatics and computing (PIC); 2015. p. 551–555.
Girvan M, Newman ME. Community structure in social and biological networks. Proc Natl Acad Sci. 2002;99(12):7821–6.
Gumbel EJ. Statistical theory of extreme values and some practical applications. NBS Appl Math Ser. 1954;33.
Hastings MB. Community detection as an inference problem. Phys. Rev. E. 2006;74(3):035102.
Jang E, Gu S, Poole B. Categorical reparameterization with Gumbel-softmax. ICLR: Toulon; 2017.
Javed MA, Younis MS, Latif S, Qadir J, Baig A. Community detection in networks: a multidisciplinary review. J Netw Comput Appl. 2018;108:87–111.
Kunegis Jérôme. KONECT—The Koblenz Network Collection. In: Proceedings of the international conference on world wide web companion; 2013. p. 1343–1350.
Li P-Z, Huang L, Wang C-D, Lai J-H. Edmot: An edge enhancement approach for motif-aware community detection. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining; 2019. p. 479–487.
Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E. 2004;69:026113.
Prat-Pérez A, Dominguez-Sal D, Larriba-Pey J-L. High quality, scalable and parallel community detection for large real graphs. In: Proceedings of the 23rd international conference on world wide web, WWW ’14, page 225–236, New York, NY, USA. Association for Computing Machinery; 2014.
Raghavan UN, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E. 2007;76(3):036106.
Rosenberg A, Hirschberg J. V-measure: A conditional entropy-based external cluster evaluation measure. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL); 2007. p. 410–420.
Rozemberczki B, Davies R, Sarkar R, Sutton C. Gemsec: Graph embedding with self clustering. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining; 2019. p. 65–72.
Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S. Community preserving network embedding. In: Thirty-first AAAI conference on artificial intelligence. 2017.
Ye F, Chen C, Zheng Z. Deep autoencoder-like nonnegative matrix factorization for community detection. In: Proceedings of the 27th ACM international conference on information and knowledge management; 2018. p. 1393–1402.
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Acharya, D.B., Zhang, H. Community Detection Clustering via Gumbel Softmax. SN COMPUT. SCI. 1, 262 (2020). https://doi.org/10.1007/s42979-020-00264-2
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DOI: https://doi.org/10.1007/s42979-020-00264-2