Skip to main content
SpringerLink
Log in
Book cover

International Conference on Advanced Data Mining and Applications

ADMA 2016: Advanced Data Mining and Applications pp 434–444Cite as

Unified Weighted Label Propagation Algorithm Using Connection Factor

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10086))

Abstract

With the social networks getting increasingly larger, fast community detection algorithms like the label propagation algorithm, are attracting more attention. But the label propagation algorithm deals vertices with no proper weight, which leads to the loss in the performance. We propose the connection factor of the vertex to measure its influence on the local connectivity. The connection factor can reveal the topological structure feature, and we propose a unified weight to modify the original label propagation algorithm. Experiments show that our Unified Weighted LPA has an average performance promotion from 5 % to 10 %, in the best case more than 30 %.

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

References

  1. Adamic, L.A., Glance, N.: The political blogosphere and the 2004 us election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 36–43. ACM (2005)

    Google Scholar 

  2. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)

    Article  MATH  Google Scholar 

  3. Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM (2002)

    Google Scholar 

  4. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 2 (2007)

    Article  Google Scholar 

  5. Leung, I.X., Hui, P., Lio, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E 79(6), 066107 (2009)

    Article  Google Scholar 

  6. Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)

    Article  Google Scholar 

  7. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42. ACM (2007)

    Google Scholar 

  8. Newman, M.E.: Modularity and community structure in networks. Proc. Nat. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  9. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)

    Article  Google Scholar 

  10. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  11. Strehl, A., Ghosh, J.: Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)

    MathSciNet  MATH  Google Scholar 

  12. Xie, J., Szymanski, B.K.: Labelrank: A stabilized label propagation algorithm for community detection in networks, pp. 138–143 (2013)

    Google Scholar 

  13. Xie, J., Szymanski, B.K., Liu, X.: Slpa: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 344–349. IEEE (2011)

    Google Scholar 

  14. Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)

    Article  Google Scholar 

  15. Zhang, X., Fei, S., Song, C., Tian, X., Ao, Y.: Label propagation algorithm based on local cycles for community detection. Int. J. Mod. Phys. B 29(5), 112–142 (2015)

    MathSciNet  Google Scholar 

Download references

Acknowledgement

This research is supported by National High-tech R&D Program of China (863 Program) under Grants 2015AA01A301, by program for New Century Excellent Talents in University’ by National Science Foundation (NSF) China 61272142, 61402492, 61402486, 61379146, 61272483, by the laboratory pre-research fund (9140C810106150C81001).

Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, People’s Republic of China

    Xin Wang, Songlei Jian, Kai Lu & Xiaoping Wang

  2. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People’s Republic of China

    Xin Wang, Songlei Jian, Kai Lu & Xiaoping Wang

Authors
  1. Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Songlei Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kai Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Wang .

Editor information

Editors and Affiliations

  1. University of Technology , Sydney, New South Wales, Australia

    Jinyan Li

  2. University of Queensland , Brisbane, Australia

    Xue Li

  3. Beijing Institute of Technology , Beijing, China

    Shuliang Wang

  4. University of Western Australia , Crawley, West Australia, Australia

    Jianxin Li

  5. University of Adelaide , Adelaide, South Australia, Australia

    Quan Z. Sheng

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Wang, X., Jian, S., Lu, K., Wang, X. (2016). Unified Weighted Label Propagation Algorithm Using Connection Factor. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49586-6_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49585-9

  • Online ISBN: 978-3-319-49586-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation