Skip to main content
SpringerLink
Log in

SpEC: Sparse Embedding-Based Community Detection in Attributed Graphs

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12114))

Included in the following conference series:

Abstract

Community detection, also known as graph clustering, is a widely studied task to find the subgraphs (communities) of related nodes in a graph. Existing methods based on non-negative matrix factorization can solve the task of both non-overlapping community detection and overlapping community detection, but the probability vector obtained by factorization is too dense and ambiguous, and the difference between these probabilities is too small to judge which community the corresponding node belongs to. This will lead to a lack of interpretability and poor performance in community detection. Besides, there are always many sparse subgraphs in a graph, which will cause unstable iterations. Accordingly, we propose SpEC (Sparse Embedding-based Community detection) for solving the above problems. First, sparse embeddings has stronger interpretability than dense ones. Second, sparse embeddings consume less space. Third, sparse embeddings can be computed more efficiently. For traditional matrix factorization-based models, their iteration update rules do not guarantee the convergence for sparse embeddings. SpEC elaborately designs the update rules to ensure convergence and efficiency for sparse embeddings. Crucially, SpEC takes full advantage of attributed graphs and learns the neighborhood patterns, which imply inherent relationships between node attributes and topological structure information. By coupled recurrent neural networks, SpEC recovers the missing edges and predicts the relationship between pairs of nodes. In addition, SpEC ensures stable convergence and improving performance. Furthermore, the results of the experiments show that our model outperforms other state-of-the-art community detection methods.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    https://github.com/wendell1996/SpEC.git.

  2. 2.

    https://linqs.soe.ucsc.edu/data.

  3. 3.

    http://snap.stanford.edu/data/index.html.

References

  1. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)

    Article  MathSciNet  Google Scholar 

  2. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  3. Bojchevski, A., Shchur, O., Zügner, D., Günnemann, S.: NetGAN: generating graphs via random walks. arXiv preprint arXiv:1803.00816 (2018)

  4. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  5. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844–3852 (2016)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. He, Z., Xie, S., Zdunek, R., Zhou, G., Cichocki, A.: Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering. IEEE Trans. Neural Netw. 22(12), 2117–2131 (2011)

    Article  Google Scholar 

  8. Hoyer, P.O.: Non-negative sparse coding. In: Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, pp. 557–565. IEEE (2002)

    Google Scholar 

  9. Hu, H., Lin, Z., Feng, J., Zhou, J.: Smooth representation clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3834–3841 (2014)

    Google Scholar 

  10. Kim, J., Park, H.: Sparse nonnegative matrix factorization for clustering. Technical report, Georgia Institute of Technology (2008)

    Google Scholar 

  11. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  12. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)

    Article  Google Scholar 

  13. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)

    Google Scholar 

  14. 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 

  15. Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. arXiv preprint arXiv:1801.07606 (2018)

  16. Li, Y., Sha, C., Huang, X., Zhang, Y.: Community detection in attributed graphs: an embedding approach. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  17. Long, B., Zhang, Z.M., Wu, X., Yu, P.S.: Relational clustering by symmetric convex coding. In: Proceedings of the 24th International Conference on Machine Learning, pp. 569–576. ACM (2007)

    Google Scholar 

  18. Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pp. 496–503 (2003)

    Google Scholar 

  19. Neumaier, A.: Solving ill-conditioned and singular linear systems: a tutorial on regularization. SIAM Rev. 40(3), 636–666 (1998)

    Article  MathSciNet  Google Scholar 

  20. Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining, pp. 977–986. ACM (2014)

    Google Scholar 

  21. Paatero, P., Tapper, U.: Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111–126 (2010)

    Article  Google Scholar 

  22. Pauca, V.P., Piper, J., Plemmons, R.J.: Nonnegative matrix factorization for spectral data analysis. Linear Algebra Appl. 416(1), 29–47 (2006)

    Article  MathSciNet  Google Scholar 

  23. Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)

    Article  Google Scholar 

  24. Shi, J., Malik, J.: Normalized cuts and image segmentation. Departmental Papers (CIS), p. 107 (2000)

    Google Scholar 

  25. Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  26. Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Min. Knowl. Disc. 22(3), 493–521 (2011)

    Article  MathSciNet  Google Scholar 

  27. Wang, X., Jin, D., Cao, X., Yang, L., Zhang, W.: Semantic community identification in large attribute networks. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 265–271 (2016)

    Google Scholar 

  28. Yang, J., Leskovec, J.: Structure and overlaps of communities in networks. arXiv preprint arXiv:1205.6228 (2012)

  29. 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 

  30. Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1151–1156. IEEE (2013)

    Google Scholar 

  31. Yang, T., Jin, R., Chi, Y., Zhu, S.: Combining link and content for community detection: a discriminative approach. In: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, pp. 927–936. ACM (2009)

    Google Scholar 

  32. You, J., Ying, R., Ren, X., Hamilton, W., Leskovec, J.: GraphRNN: generating realistic graphs with deep auto-regressive models. In: International Conference on Machine Learning, pp. 5694–5703 (2018)

    Google Scholar 

Download references

Acknowledgment

This work is supported in part by the National Natural Science Foundation of China Projects No. U1936213, U1636207, the National Science Foundation Projects No. 1565137, 1829071, the Shanghai Science and Technology Development Fund No. 19DZ1200802, 19511121204.

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

    Huidi Chen, Yun Xiong & Yangyong Zhu

  2. Shanghai Institute for Advanced Communication and Data Science, Fudan University, Shanghai, China

    Huidi Chen, Yun Xiong & Yangyong Zhu

  3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Changdong Wang

  4. University of California, Los Angeles, CA, USA

    Wei Wang

Authors
  1. Huidi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Changdong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yangyong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Xiong .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of Systems Engineering and En, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Steven Euijong Whang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, H., Xiong, Y., Wang, C., Zhu, Y., Wang, W. (2020). SpEC: Sparse Embedding-Based Community Detection in Attributed Graphs. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59419-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59418-3

  • Online ISBN: 978-3-030-59419-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation