Skip to main content
SpringerLink
Log in

Community Detection in Online Social Network Using Graph Embedding and Hierarchical Clustering

  • Conference paper
  • First Online:
Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18) (IITI'18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 874))

  • 453 Accesses

Abstract

The community detection plays an important role in social network analysis. It can be used to find users that behave in a similar manner, detect groups of interests, cluster users in e-commerce application such as their taste or shopping habits, etc. In this paper, we proposed an algorithm to detect the community in online social networks. Our algorithm represents the nodes and the relationships in the social networks using a vector, agglomerative clustering (the most famous clustering algorithm) will cluster those vectors to figure out the communities. The experimental results show that our algorithm performs better traditional agglomerative clustering because of the ability to detect the community which has better modularity value.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  2. Hofmann, T., Buhmann, J.M.: Multidimensional scaling and data clustering. In: NIPS, pp. 459–466 (1994)

    Google Scholar 

  3. Chen, M., Tsang, I.W., Tan, M., Jen, C.T.: A unified feature selection framework for graph embedding on high dimensional data. IEEE Trans. Knowl. Data Eng. 27(6), 1465–1477 (2015)

    Article  Google Scholar 

  4. Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)

    Article  Google Scholar 

  5. Zhou, C., Liu, Y., Liu, X., Liu, Z., Gao, J.: Scalable graph embedding for asymmetric proximity. In: AAAI, pp. 2942–2948 (2017)

    Google Scholar 

  6. Feng, J., Huang, M., Yang, Y., Zhu, X.: GAKE: graph aware knowledge embedding. In: COLING, pp. 641–651 (2016)

    Google Scholar 

  7. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: WWW, pp. 1067–1077 (2015)

    Google Scholar 

  8. Xie, R., Liu, Z., Sun, M.: Representation learning of knowledge graphs with hierarchical types. In: IJCAI, pp. 2965–2971 (2016)

    Google Scholar 

  9. Alharbi, B., Zhang, X.: Learning from your network of friends: a trajectory representation learning model based on online social ties. In: ICDM, pp. 781–786 (2016)

    Google Scholar 

  10. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: KDD, pp. 701–710 (2014)

    Google Scholar 

  11. Yang, Z., Tang, J., Cohen, W.: Multi-modal Bayesian embeddings for learning social knowledge graphs. In: IJCAI, pp. 2287–2293 (2016)

    Google Scholar 

  12. Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. In: ACL (2016)

    Google Scholar 

  13. Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: IJCAI, pp. 1895–1901 (2016)

    Google Scholar 

  14. Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: KDD, pp. 135–144 (2017)

    Google Scholar 

  15. Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: KDD, pp. 1225–1234 (2016)

    Google Scholar 

  16. Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014–2023, 11 June 2016

    Google Scholar 

  17. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  18. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)

    Article  Google Scholar 

  19. Barnes, E.R.: An algorithm for partitioning the nodes of a graph. SIAM J. Algebr. Discret. Methods 3(4), 541–550 (1982)

    Article  MathSciNet  Google Scholar 

  20. Shahriar, F., Nesar, A.B., Mahbub, N.M., Shatabda, S.: EGAGP: an enhanced genetic algorithm for producing efficient graph partitions. In: 2017 4th International Conference on Networking, Systems and Security (NSysS), pp. 1–9. IEEE, 18 December 2017

    Google Scholar 

  21. Reijnders, B.J.: Pre-processing Road Networks for Graph Partitioning Using Edge-Betweenness Centrality. Bachelor’s thesis

    Google Scholar 

  22. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, no. 14, pp. 281–297, 21 June 1967

    Google Scholar 

  23. Hlaoui, A., Wang, S.: A direct approach to graph clustering. Neural Netw. Comput. Intell. 4(8), 158–163 (2004)

    Google Scholar 

  24. Bezdek, J.C.: Objective Function Clustering. In: Pattern Recognition with Fuzzy Objective Function Algorithms, pp. 43–93. Springer, Boston (1981)

    Chapter  Google Scholar 

  25. Donath, W.E., Hoffman, A.J.: Lower bounds for the partitioning of graphs. IBM J. Res. Dev. 17(5), 420–425 (1973)

    Article  MathSciNet  Google Scholar 

  26. Li, L., Wang, S., Xu, S., Yang, Y.: Constrained spectral clustering using nyström method. Procedia Comput. Sci. 31(129), 9–15 (2018)

    Article  Google Scholar 

  27. Bhattacharyya, S., Chatterjee, S.: Spectral clustering for multiple sparse networks: I. arXiv preprint arXiv:1805.10594, 27 May 2018

  28. Li, X., Kao, B., Luo, S., Ester, M.: ROSC: robust spectral clustering on multi-scale data. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 157–166. International World Wide Web Conferences Steering Committee, 10 April 2018

    Google Scholar 

  29. Sharma, A., López, Y., Tsunoda, T.: Divisive hierarchical maximum likelihood clustering. BMC Bioinform. 18(16), 546 (2017)

    Article  Google Scholar 

  30. Murata, T.: Detecting communities in social networks. In: Handbook of Social Network Technologies and Applications, pp. 269–280. Springer, Boston (2010)

    Chapter  Google Scholar 

  31. Madani, F.: ‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis. Scientometrics 105(1), 323–335 (2015)

    Article  Google Scholar 

  32. 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, pp. 855–864. ACM, 13 August 2016

    Google Scholar 

  33. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, vol. 1. Np. Springer, New York (2001)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Vang Le

  2. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

Authors
  1. Vang Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vaclav Snasel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vang Le .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  3. Bauman Moscow State Technical University, Moscow, Russia

    Valery Tarassov

  4. VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  5. Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Le, V., Snasel, V. (2019). Community Detection in Online Social Network Using Graph Embedding and Hierarchical Clustering. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-01818-4_26

Download citation

Publish with us

Policies and ethics

Search

Navigation