Article

Community Detection in Social Networks Using Deep Learning

Authors:

S. Durga BhavaniAuthors Info & Claims

Distributed Computing and Internet Technology: 16th International Conference, ICDCIT 2020, Bhubaneswar, India, January 9–12, 2020, Proceedings

Pages 241 - 250

https://doi.org/10.1007/978-3-030-36987-3_15

Published: 09 January 2020 Publication History

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.

References

[1]

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]

Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: JMLR: Workshop and Conference Proceedings, vol. 27, pp. 37–50 (2012)

[3]

Blondel, V., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008 (2008)

[4]

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]

Newman MEJ Fast algorithm for detecting community structure in networks Phys. Rev. E 2004 69 066133

[6]

Newman MEJ Modularity and community structure in networks Proc. Natl. Acad. Sci. U.S.A. 2006 103 8577-8582

[7]

Fortunato SantoCommunity detection in graphsPhysics Reports20104863-575-1742580414

[8]

Girvan M and Newman MEJCommunity structure in social and biological networksProc. Natl. Acad. Sci.2001997821-78261908073

[9]

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]

Hamilton WL, Ying Z, and Leskovec J Representation learning on graphs: methods and applications IEEE Data Eng. Bull. 2017 40 52-74

[11]

He, D., Liu, D., Jin, D., Zhang, W.: A stochastic model for detecting heterogeneous link communities in complex networks. In: AAAI (2015)

[12]

Jia, Y., Zhang, Q., Zhang, W., Wang, X.: CommunityGAN: community detection with generative adversarial nets, January 2019

[13]

Jin, D., Chen, Z., He, D., Zhang, W.: Modeling with node degree preservation can accurately find communities. In: AAAI (2015)

[14]

Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR (2016)

[15]

Lusseau D and Newman M Identifying the role that animals play in their social networks Proc. Biol. Sci. 2004 271 Suppl 6 S477-S481

[16]

Newman M Networks: An Introduction 2010 Oxford Oxford University Press

[17]

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]

Psorakis I, Roberts S, Ebden M, and Sheldon B Overlapping community detection using Bayesian non-negative matrix factorization Phys. Rev. E 2011 83 066114

[19]

Vincent P, Larochelle H, Lajoie I, Bengio Y, and Manzagol PAStacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterionJ. Mach. Learn. Res.2010113371-340827561881242.68256

[20]

Vu Tran, P.: Learning to make predictions on graphs with autoencoders. CoRR abs/1802.08352, pp. 237–245, October 2018

[21]

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

Cited By

de Silva AChen AMa HNekooei M(2022)Genetic Algorithm with a Novel Leiden-based Mutation Operator for Community DetectionAI 2022: Advances in Artificial Intelligence10.1007/978-3-031-22695-3_18(252-265)Online publication date: 5-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-22695-3_18
Al-Andoli MCheah WTan S(2021)Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithmsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20134240:3(4517-4533)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-201342

Index Terms

Community Detection in Social Networks Using Deep Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

Recommendations

Hidden community detection in social networks

Propose a new conception of hidden community for network analysis.Provide a meta-approach called HICODE for finding the hidden communities.Several weakening methods are proposed to reduce the impact of the detected structure.The framework works ...
Community detection in social networks using structural and content information
iiWAS2018: Proceedings of the 20th International Conference on Information Integration and Web-based Applications & Services

Community detection in social networks is an area, which has witnessed many studies in recent years, and therefore several algorithms have been proposed. The majority of these methods are based on the relationships among users (structural information) ...
Community detection in networks: A multidisciplinary review
Abstract
The modern science of networks has made significant advancement in the modeling of complex real-world systems. One of the most important features in these networks is the existence of community structure. In recent years, many ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

Distributed Computing and Internet Technology: 16th International Conference, ICDCIT 2020, Bhubaneswar, India, January 9–12, 2020, Proceedings

Jan 2020

442 pages

ISBN:978-3-030-36986-6

DOI:10.1007/978-3-030-36987-3

Editors:
Dang Van Hung
Vietnam National University, Hanoi, Vietnam
,
Meenakshi D´Souza
International Institute of Information Technology, Bangalore, India

© Springer Nature Switzerland AG 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 09 January 2020

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

de Silva AChen AMa HNekooei M(2022)Genetic Algorithm with a Novel Leiden-based Mutation Operator for Community DetectionAI 2022: Advances in Artificial Intelligence10.1007/978-3-031-22695-3_18(252-265)Online publication date: 5-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-22695-3_18
Al-Andoli MCheah WTan S(2021)Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithmsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20134240:3(4517-4533)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-201342

View Options

View options

Media

Figures

Other

Tables

View Table of Contents