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2020 | OriginalPaper | Buchkapitel

Community Detection in Social Networks Using Deep Learning

verfasst von : M. Dhilber, S. Durga Bhavani

Erschienen in: Distributed Computing and Internet Technology

Verlag: Springer International Publishing

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Abstract

Community structure is found everywhere from simple networks to real world complex networks. The problem of community detection is to predict clusters of nodes that are densely connected among themselves. The task of community detection has a wide variety of applications ranging from recommendation systems, advertising, marketing, epidemic spreading, cancer detection etc. The two mainly existing approaches for community detection, namely, stochastic block model and modularity maximization model focus on building a low dimensional network embedding to reconstruct the original network structure. However the mapping to low dimensional space in these methods is purely linear. Understanding the fact that real world networks contain non-linear structures in abundance, aforementioned methods become less practical for real world networks. Considering the nonlinear representation power of deep neural networks, several solutions based on autoencoders are being proposed in the recent literature. In this work, we propose a deep neural network architecture for community detection wherein we stack multiple autoencoders and apply parameter sharing. This method of training autoencoders has been successfully applied for the problems of link prediction and node classification in the literature. Our enhanced model with modified architecture produced better results compared to many other existing methods. We tested our model on a few benchmark datasets and obtained competitive results.

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Literatur
1.
Zurück zum Zitat Adamic, L.A.: The political blogosphere and the 2004 U.S. election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, April 2005 Adamic, L.A.: The political blogosphere and the 2004 U.S. election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, April 2005
2.
Zurück zum Zitat Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: JMLR: Workshop and Conference Proceedings, vol. 27, pp. 37–50 (2012) Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: JMLR: Workshop and Conference Proceedings, vol. 27, pp. 37–50 (2012)
3.
Zurück zum Zitat Blondel, V., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008 (2008) Blondel, V., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008 (2008)
4.
Zurück zum Zitat Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in Neural Information Processing Systems, vol. 6, February 1994 Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in Neural Information Processing Systems, vol. 6, February 1994
5.
Zurück zum Zitat Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004). Statistical, nonlinear, and soft matter physicsCrossRef Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004). Statistical, nonlinear, and soft matter physicsCrossRef
6.
Zurück zum Zitat Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A. 103, 8577–8582 (2006)CrossRef Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A. 103, 8577–8582 (2006)CrossRef
8.
Zurück zum Zitat Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2001)MathSciNetCrossRef Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2001)MathSciNetCrossRef
9.
Zurück zum Zitat Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 855–864. ACM, New York, NY, USA (2016) Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 855–864. ACM, New York, NY, USA (2016)
10.
Zurück zum Zitat Hamilton, W.L., Ying, Z., Leskovec, J.: Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40, 52–74 (2017) Hamilton, W.L., Ying, Z., Leskovec, J.: Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40, 52–74 (2017)
11.
Zurück zum Zitat He, D., Liu, D., Jin, D., Zhang, W.: A stochastic model for detecting heterogeneous link communities in complex networks. In: AAAI (2015) He, D., Liu, D., Jin, D., Zhang, W.: A stochastic model for detecting heterogeneous link communities in complex networks. In: AAAI (2015)
12.
Zurück zum Zitat Jia, Y., Zhang, Q., Zhang, W., Wang, X.: CommunityGAN: community detection with generative adversarial nets, January 2019 Jia, Y., Zhang, Q., Zhang, W., Wang, X.: CommunityGAN: community detection with generative adversarial nets, January 2019
13.
Zurück zum Zitat Jin, D., Chen, Z., He, D., Zhang, W.: Modeling with node degree preservation can accurately find communities. In: AAAI (2015) Jin, D., Chen, Z., He, D., Zhang, W.: Modeling with node degree preservation can accurately find communities. In: AAAI (2015)
14.
Zurück zum Zitat Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR (2016) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR (2016)
15.
Zurück zum Zitat Lusseau, D., Newman, M.: Identifying the role that animals play in their social networks. Proc. Biol. Sci. 271(Suppl 6), S477–S481 (2004) Lusseau, D., Newman, M.: Identifying the role that animals play in their social networks. Proc. Biol. Sci. 271(Suppl 6), S477–S481 (2004)
16.
Zurück zum Zitat Newman, M.: Networks: An Introduction. Oxford University Press, Oxford (2010)CrossRef Newman, M.: Networks: An Introduction. Oxford University Press, Oxford (2010)CrossRef
17.
Zurück zum Zitat Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, March 2014 Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, March 2014
18.
Zurück zum Zitat Psorakis, I., Roberts, S., Ebden, M., Sheldon, B.: Overlapping community detection using Bayesian non-negative matrix factorization. Phys. Rev. E 83, 066114 (2011). Statistical, nonlinear, and soft matter physicsCrossRef Psorakis, I., Roberts, S., Ebden, M., Sheldon, B.: Overlapping community detection using Bayesian non-negative matrix factorization. Phys. Rev. E 83, 066114 (2011). Statistical, nonlinear, and soft matter physicsCrossRef
19.
Zurück zum Zitat Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetMATH Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetMATH
20.
Zurück zum Zitat Vu Tran, P.: Learning to make predictions on graphs with autoencoders. CoRR abs/1802.08352, pp. 237–245, October 2018 Vu Tran, P.: Learning to make predictions on graphs with autoencoders. CoRR abs/1802.08352, pp. 237–245, October 2018
21.
Zurück zum Zitat Yang, L., Cao, X., He, D., Wang, C., Wang, X., Zhang, W.: Modularity based community detection with deep learning. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp. 2252–2258, January 2016 Yang, L., Cao, X., He, D., Wang, C., Wang, X., Zhang, W.: Modularity based community detection with deep learning. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp. 2252–2258, January 2016
Metadaten
Titel
Community Detection in Social Networks Using Deep Learning
verfasst von
M. Dhilber
S. Durga Bhavani
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-36987-3_15