Autoencoder Based Community Detection with Adaptive Integration of Network Topology and Node Contents

  • Jinxin Cao
  • Di JinEmail author
  • Jianwu Dang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)


Community detection plays an important role in understanding the structure and laws of social networks. Many community detection approaches have been proposed and focus on topological structure alone. In addition to topology, node contents exist in real-world networks, and may help for community detection. Recently, some studies try to combine topological structure and node contents. However, it is difficult to address an inherent situation in real- world networks, that is the mismatch between topological structure and node contents in term of community patterns. When considering both topology and content of networks, the performance of those community detection methods is often limited by this mismatch. Besides, networks are often full of nonlinear features, making those methods less effective in practice. In this paper, we present an adaptive method for community detection, which is reached by a graph regularized autoencoder approach. This new method introduces a novel adaptive parameter to achieve robust integration of the topological and content information when there exists the mismatch between those two types of information in term of communities. Experiments on both synthetic networks and real-world networks further indicate that the proposed new method exhibits more robust behavior and outperforms the leading methods when there exists the mismatch between topology and content.


Community detection Node contents Autoencoder Mismatch 



The work was supported by the National Key R&D Program of China (2017YFC0820106), the National Basic Research Program of China (2013CB329301), and the Natural Science Foundation of China (61772361).


  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.
    Jin, D., Wang, X., Hem, R., et al.: Robust detection of link communities in large social networks by exploiting link semantics. In: 32th AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA (2018)Google Scholar
  3. 3.
    He, D., Feng, Z., Jin, D., et al.: Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In: 31th AAAI Conference on Artificial Intelligence, San Francisco, California, USA (2017)Google Scholar
  4. 4.
    Papadimitriou, C.H., Raghavan, P., Tamaki, H., et al.: Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61(2), 217–235 (2000)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 587–596. ACM (2013)Google Scholar
  6. 6.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  7. 7.
    Derrida, B., Gardner, E., Zippelius, A.: An exactly solvable asymmetric neural network model. EPL (Europhys. Lett.) 4(2), 167 (1987)CrossRefGoogle Scholar
  8. 8.
    Qin, M., Jin, D., He, D., et al.: Adaptive community detection incorporating topology and content in social networks. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 675–682. ACM (2017)Google Scholar
  9. 9.
    Cao, J., Jin, D., Yang, L., et al.: Incorporating network structure with node contents for community detection on large networks using deep learning. Neurocomputing 297, 71–81 (2018)CrossRefGoogle Scholar
  10. 10.
    Tian, F., Gao, B., Cui, Q., et al.: Learning deep representations for graph clustering. In: 28th AAAI Conference on Artificial Intelligence, pp. 1293–1299 (2014)Google Scholar
  11. 11.
    Yang, L., Cao, X., He, D., et al.: Modularity based community detection with deep learning. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2252–2258, New York, USA (2016)Google Scholar
  12. 12.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Ester, M., Ge, R., Gao, B.J., et al.: Joint cluster analysis of attribute data and relationship data: the connected k-center problem. In: Proceedings of the 2006 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 246–257 (2006)Google Scholar
  14. 14.
    Yu, J., Zhang, R., Gao, Y., et al.: Modularity-based dynamic clustering for energy efficient UAVs aided communications. IEEE Wirel. Commun. Lett. 2018, 1–5 (2018)Google Scholar
  15. 15.
    Yang, J., Mcauley, J., Leskovec, J., et al.: Community detection in networks with node attributes. In: The IEEE International Conference on Data Mining series (ICDM), pp. 1151–1156, Dallas, Texas, USA (2013)Google Scholar
  16. 16.
    Balasubramanyan, R., Cohen, W.W.: Block-LDA: jointly modeling entity-annotated text and entity-entity links. In: Proceedings of the 2011 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 450–461 (2011)Google Scholar
  17. 17.
    Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E: Stat. Nonlinear Soft Matter Phys. 69, 066133 (2003)CrossRefGoogle Scholar
  18. 18.
    Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1(3), 211–218 (1963)CrossRefGoogle Scholar
  19. 19.
    Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539–547 (2012)Google Scholar
  20. 20.
    Wang, X., Jin, D., Cao, X., et al.: Semantic community identification in large attribute networks. In: 30th AAAI Conference on Artificial Intelligence, pp. 265–271, Phoenix, Arizona, USA (2016)Google Scholar
  21. 21.
    Yang, T., Jin, R., Chi, Y., et al.: Combining link and content for community detection: a discriminative approach. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 927–936. ACM (2009)Google Scholar
  22. 22.
    Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.School of Information ScienceJapan Advanced Institute of Science and TechnologyNomiJapan

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