A Network Embedding-Enhanced Approach for Generalized Community Detection

  • Dongxiao He
  • Xue Yang
  • Zhiyong Feng
  • Shizhan ChenEmail author
  • Françoise Fogelman-Soulié
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)


Community detection is one of the most important tasks in network analysis. Many community detection methods have been proposed recently. However, they typically focus on assortative community structures (i.e. nodes within the same community have more connections), while ignoring the diversity of community patterns in real world. In addition, the network topology, which these methods are mainly based on, is often noisy and very sparse. These two issues bring difficulties to existing methods for accurately finding communities. To address these problems, we propose a new probabilistic generative model. In this model, we first use an idea of mixture modeling to describe network regularities, and then introduce network embeddings to further enhance the ability of this model to describe network communities. Based on these, the new model will not only find generalized communities (e.g. assortative communities, disassortative communities, and their mixture), but also be robust for community detection in complicated situations (e.g. on very sparse networks with large noise). We present an efficient expectation-maximization (EM) algorithm to learn the model. Finally, we demonstrate the superior performance of our new approach over some state-of-the-art methods on both synthetic and real networks, and also validate its robustness to the above issues via a case study analysis.


Community detection Generalized communities Network embedding Probabilistic generative model EM algorithm 



This work was supported by the National Key R&D Program of China grant No. 2017YFB1401201, the National Natural Science Foundation of China grants No. 61502334, 61572350, 61672377, and the Elite Scholar Program of Tianjin University grant No. 2017XRG-0016.


  1. 1.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Yang, L., Cao, X., He, D., Zhang, W.: Modularity based community detection with deep learning. In: Proceedings of 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 2252–2258. AAAI Press, New York (2016)Google Scholar
  3. 3.
    Jin, D., Chen, Z., He, D., Zhang, W.: Modeling with node degree preservation can accurately find communities. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. AAAI, Austin (2015)Google Scholar
  4. 4.
    Jia, S., et al.: Defining and identifying cograph communities in complex networks. New J. Phys. 17 (2015)Google Scholar
  5. 5.
    Chen, P., Wu, L.: Revisiting spectral graph clustering with generative community models. In: IEEE International Conference on Data Mining, pp. 51–60 (2017)Google Scholar
  6. 6.
    Liu, F., Choi, D., Xie, L., Roeder, K.: Global spectral clustering in dynamic networks. Proc. Natl. Acad. Sci. (2018)Google Scholar
  7. 7.
    Martin, T., Ball, B., Newman, M.E.J.: Structural inference for uncertain networks. Phys. Rev. E 93(1), 012306 (2016)CrossRefGoogle Scholar
  8. 8.
    He, D., Feng, Z., Jin, D., Wang, X., Zhang, W.: Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In: Proceedings of the 31th AAAI Conference on Artificial Intelligence. AAAI, San Francisco (2017)Google Scholar
  9. 9.
    Jin, D., Wang, X., He, R., He, D., Dang, J., Zhang, W.: Robust detection of link communities in large social networks by exploiting link semantics. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI, New Orleans (2018)Google Scholar
  10. 10.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Newman, M.E.J., Peixoto, T.P.: Generalized communities in networks. Phys. Rev. Lett. 115(8), 088701 (2015)CrossRefGoogle Scholar
  12. 12.
    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 (2016)Google Scholar
  13. 13.
    Newman, M.E.J.: Real-world network data in Newman’s homepage (2017).
  14. 14.
    Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state of the art and comparative study. ACM Comput. Surv. 45, 1–35 (2013)CrossRefGoogle Scholar
  15. 15.
    Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–106 (2008)CrossRefGoogle Scholar
  16. 16.
    Karrer, B., Newman, M.E.J.: Stochastic block models and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Min. Knowl. Discov. 22(3), 493–521 (2011)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhang, Y., Yeung, D.Y.: Overlapping community detection via bounded nonnegative matrix factorization. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 606–614. ACM Press, New York (2012)Google Scholar
  19. 19.
    Newman, M.E., Leicht, E.: Mixture models and exploratory analysis in networks. Proc. Natl. Acad. Sci. U.S.A. 104(23), 9564 (2006)CrossRefGoogle Scholar
  20. 20.
    Liu, H., Wu, Z., Li, X., Cai, D., Huang, T.: Constrained nonnegative matrix factorization for image representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1299–1311 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dongxiao He
    • 1
    • 2
  • Xue Yang
    • 1
    • 2
  • Zhiyong Feng
    • 1
    • 3
  • Shizhan Chen
    • 1
    • 2
    Email author
  • Françoise Fogelman-Soulié
    • 3
  1. 1.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjin UniversityTianjinChina
  2. 2.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  3. 3.School of Computer SoftwareTianjin UniversityTianjinChina

Personalised recommendations