Skip to main content
SpringerLink
Log in
Book cover

Pacific Rim International Conference on Artificial Intelligence

PRICAI 2018: PRICAI 2018: Trends in Artificial Intelligence pp 643–657Cite as

Accurately Detecting Community with Large Attribute in Partial Networks

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11012))

Abstract

A community structure is the most significant feature of social networks. Fusing the relation information and the attribute information to is necessary to detect community in the attributed social network. However, both relation and attribute information will have non-uniform quality because of the meaningless or erroneous noise in a social network. Moreover, the nodes that lose relation or attribute information will make the network into a partial network. In those cases, it is unrealistic to split users into different communities correctly without considering the noise and incompleteness in combination processing. To solve this problem, we propose a non-negative matrix factorization (NMF)-based community detection framework. In this framework, common and correct community structures can be identified effectively and disagreements can be reconciled by introducing two regularizations in combination processing. The experimental results confirm the superior performance of the method and demonstrate its effectiveness for a partial network.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Notes

  1. 1.

    http://mlg.ucd.ie/aggregation/.

  2. 2.

    http://www.cs.umd.edu/projects/linqs/projects/lbc/.

References

  1. Yang, L., Jin, D., He, D., Fu, H., Cao, X., Fogelman-Soulie, F.: Improving the efficiency and effectiveness of community detection via prior-induced equivalent super-network. Sci. Rep. 7, 634 (2017)

    Article  Google Scholar 

  2. Porter, M.A., Onnela, J.P., Mucha, P.J.: Communities in networks. Not. AMS 56, 4294–4303 (2009)

    MathSciNet  MATH  Google Scholar 

  3. Tang, J., Chang, S., Aggarwal, C., Liu, H.: Negative link prediction in social media, pp. 87–96 (2014)

    Google Scholar 

  4. Tang, J., Aggarwal, C., Liu, H.: Recommendations in signed social networks. In: the 25th International World Wide Web Conference, pp. 31–40 (2016)

    Google Scholar 

  5. Cecile, B., David, C.J., Matteo, M., Barbora, M.: Clustering attributed graphs: models, measures and methods. Netw. Sci. 3, 408–444 (2015)

    Article  Google Scholar 

  6. Liu, X., Wang, W., He, D., Jiao, P., Jin, D., Cannistraci, C.V.: Semi-supervised community detection based on non-negative matrix factorization with node popularity. Inf. Sci. 381, 304–321 (2017)

    Article  Google Scholar 

  7. Greene, D.: Producing a unified graph representation from multiple social network views. In: the 5th Annual ACM Web Science Conference, pp. 118–121. ACM (2013)

    Google Scholar 

  8. Fang, Y., Cheng, R., Luo, S., Hu, J.: Effective community search for large attributed graphs. Proc. VLDB Endow. 9, 1233–1244 (2016)

    Article  Google Scholar 

  9. Pei, Y., Chakraborty, N., Sycara, K.: Nonnegative matrix tri-factorization with graph regularization for community detection in social networks. In: International Conference on Artificial Intelligence, pp. 2083–2089 (2015)

    Google Scholar 

  10. Yang, J., Mcauley, J., Leskovec, J.: Community detection in networks with node attributes, pp. 1151–1156 (2014)

    Google Scholar 

  11. Huang, X., Cheng, H., Yu, J.X.: Dense community detection in multi-valued attributed networks. Inf. Sci. 314, 77–99 (2015)

    Article  Google Scholar 

  12. Xu, Z., Ke, Y., Wang, Y., Cheng, H., Cheng, J.: A model-based approach to attributed graph clustering. In: The 2012 ACM SIGMOD International Conference on Management of Data, pp. 505–516. ACM (2012)

    Google Scholar 

  13. Palchykov, V., Gemmetto, V., Boyarsky, A., Garlaschelli, D.: Ground truth? Concept-based communities versus the external classification of physics manuscripts. EPJ Data Sci. 5, 28 (2016)

    Article  Google Scholar 

  14. Peng, C., Kang, Z., Cheng, Q.: Integrating feature and graph learning with low-rank representation. Neurocomputing 249, 106–116 (2017)

    Article  Google Scholar 

  15. Li, S.Y., Jiang, Y., Zhou, Z.H.: Partial multi-view clustering. In: AAAI Conference on Artificial Intelligence (AAAI 2014) (2014)

    Google Scholar 

  16. Shao, W., He, L., Yu, P.S.: Multiple incomplete views clustering via weighted nonnegative matrix factorization with L2,1 regularization. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 318–334. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23528-8_20

    Chapter  Google Scholar 

  17. Xiang, J., et al.: Enhancing community detection by local structural information. 2016, 33–45 (2016)

    Google Scholar 

  18. Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548–1560 (2011)

    Article  Google Scholar 

  19. Lee, K., Caverlee, J., Cheng, Z., Sui, D.Z.: Campaign extraction from social media. ACM Trans. Intell. Syst. Technol. 5, 1–28 (2014)

    Article  Google Scholar 

  20. 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 (2011)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  22. Wu, W., Kwong, S., Zhou, Y., Jia, Y., Gao, W.: Nonnegative matrix factorization with mixed hypergraph regularization for community detection. Inf. Sci. 435, 263–281 (2018)

    Article  MathSciNet  Google Scholar 

  23. Masuda, N., Porter, M.A., Lambiotte, R.: Random walks and diffusion on networks. Physics reports (2017)

    Google Scholar 

  24. Qin, X., Dai, W., Jiao, P., Wang, W., Ning, Y.: A multi-similarity spectral clustering method for community detection in dynamic networks. Sci. Rep. 6, 31–54 (2016)

    Google Scholar 

  25. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems 13, NIPS, pp. 556–562 (2000)

    Google Scholar 

  26. Liu, X., Wei, Y.M., Wang, J., Wang, W.J., He, D.X., Song, Z.J.: Community detection enhancement using non-negative matrix factorization with graph regularization. Int. J. Mod. Phys. B 30, 130 (2016)

    MathSciNet  MATH  Google Scholar 

  27. Ma, X., Sun, P., Qin, G.: Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability (2017)

    Article  Google Scholar 

  28. Ozer, M., Kim, N., Davulcu, H.: Community detection in political Twitter networks using Nonnegative Matrix Factorization methods. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 81–88 (2016)

    Google Scholar 

  29. Jin, D., Chen, Z., He, D., Zhang, W.: Modeling with node degree preservation can accurately find communities. New Media Soc. 18, 1293–1309 (2016)

    Article  Google Scholar 

  30. 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 

  31. Whang, J.J., Gleich, D.F., Dhillon, I.S.: Overlapping community detection using neighborhood-inflated seed expansion. 28, 1272–1284 (2015)

    Google Scholar 

  32. Bai, L., Cheng, X., Liang, J., Guo, Y.: Fast graph clustering with a new description model for community detection. Inf. Sci. 388, 37–47 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  34. Chen, Y., Rege, M., Dong, M., Hua, J.: Non-negative matrix factorization for semi-supervised data clustering. Knowl. Inf. Syst. 17, 355–379 (2008)

    Article  Google Scholar 

  35. Xia, R., Pan, Y., Du, L., Yin, J.: Robust multi-view spectral clustering via low-rank and sparse decomposition. In: The 28th AAAI Conference on Artificial Intelligence, pp. 2149–2155 (2014)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National High Technology Research and Development Program of 2016YFB0100903 and the Beijing Municipal Science and Technology Commission Special Major (D171100005017002, D171100005117002) and the National Natural Science Foundation of China (U1664263).

Author information

Authors and Affiliations

  1. Tsinghua University, Beijing, 100084, China

    Wei Han & Xinyu Zhang

  2. National University of Defense Technology, Xi’an, 710106, China

    Guopeng Li

  3. Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China

    Wei Han

Authors
  1. Wei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guopeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinyu Zhang .

Editor information

Editors and Affiliations

  1. Southeast University, Nanjing, China

    Xin Geng

  2. University of Tasmania, Hobart, Tasmania, Australia

    Byeong-Ho Kang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Han, W., Li, G., Zhang, X. (2018). Accurately Detecting Community with Large Attribute in Partial Networks. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97304-3_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97303-6

  • Online ISBN: 978-3-319-97304-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation