Skip to main content
SpringerLink
Log in

A Multi-label Propagation Community Detection Algorithm for Dynamic Complex Networks

  • Conference paper
  • First Online:
Advanced Information Systems Engineering (CAiSE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12751))

Included in the following conference series:

Abstract

With the rapid development of the Internet, the complex network data presents an explosive growth. However, most of the complex networks in the real world are dynamic, How to effectively detect communities in dynamic complex networks has become a hot issue in current research. Therefore, we propose a dynamic network oriented multi-label propagation algorithm. Firstly, in order to reduce the running time, the SLPA algorithm of multi-label propagation class is selected as the basic algorithm; secondly, the SLPA algorithm is improved by using the history label to initialize the labels, and then the DSLPA (Speaker-listener Label Propagation Algorithm for Dynamic network) algorithm is designed and implemented. The experimental results showed that the proposed algorithm has high modularity and greatly reduces the running time.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

References

  1. He, J., Chen, D.: A fast algorithm for community detection in temporal network. Phys. A Stat. Mech. Appl. 429, 87–94 (2015)

    Article  Google Scholar 

  2. Wang, Z., Zhang, D., Zhou, X., Yang, D., Yu, Z., Yu, Z.: Discovering and profiling overlapping communities in location-based social networks. IEEE Trans. Syst. Man Cybern. Syst. 44(4), 499–509 (2013)

    Article  Google Scholar 

  3. Rhouma, D., Ben Romdhane, L.: An efficient multilevel scheme for coarsening large scale social networks. Appl. Intell. 48(10), 3557–3576 (2018)

    Article  Google Scholar 

  4. LaSalle, D., Karypis, G.: Multi-threaded modularity based graph clustering using the multilevel paradigm. J. Parallel Distrib. Comput. 76, 66–80 (2015)

    Article  Google Scholar 

  5. Li, Z., Liu, J., Kai, W.: A multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks. IEEE Trans. Cybern. 48(7), 1963–1976 (2017)

    Article  Google Scholar 

  6. Zhang, H., Dong, B., Feng, B., Wu, H.: An overlapping community detection algorithm based on triangle reduction weighted for large-scale complex network. In: Qiu, M. (ed.) ICA3PP 2020. LNCS, vol. 12452, pp. 627–644. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60245-1_43

    Chapter  Google Scholar 

  7. Cordeiro, M., Sarmento, R.P., Gama, J.: Dynamic community detection in evolving networks using locality modularity optimization. Social Netw. Anal. Min. 6(1), 1–20 (2016). https://doi.org/10.1007/s13278-016-0325-1

    Article  Google Scholar 

  8. Clementi, A., Di. Ianni, M., Gambosi, G., Natale, E., Silvestri, R.: Distributed community detection in dynamic graphs. Theor. Comput. Sci. 584, 19–41 (2015)

    Article  MathSciNet  Google Scholar 

  9. Zhan, B., Zhiang, W., Cao, J., Jiang, Y.: Local community mining on distributed and dynamic networks from a multiagent perspective. IEEE Trans. Cybern. 46(4), 986–999 (2015)

    Google Scholar 

  10. Samie, M.E., Hamzeh, A.: Change-aware community detection approach for dynamic social networks. Appl. Intell. 48(1), 78–96 (2017). https://doi.org/10.1007/s10489-017-0934-z

    Article  Google Scholar 

  11. Chen, N., Bo, H., Rui, Y.: Dynamic network community detection with coherent neighborhood propinquity. IEEE Access 8, 27915–27926 (2020)

    Article  Google Scholar 

  12. Zeng, X., Wang, W., Chen, C., Yen, G.G.: A consensus community-based particle swarm optimization for dynamic community detection. IEEE Trans. Cybern. 50(6), 2502–2513 (2019)

    Article  Google Scholar 

  13. Bansal, S., Bhowmick, S., Paymal, P.: Fast community detection for dynamic complex networks. In: da F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds.) CompleNet 2010. CCIS, vol. 116, pp. 196–207. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25501-4_20

    Chapter  Google Scholar 

  14. Chayant, T., Tanya, B., David, K.: A framework for community identification in dynamic social networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 717–726 (2007)

    Google Scholar 

  15. Lin, Y.R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L.: FacetNet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th International Conference on World Wide Web, pp. 685–694 (2008)

    Google Scholar 

  16. Nguyen, N.P., Dinh, T.N., Shen, Y., Thai, M.T.: Dynamic social community detection and its applications. PloS One 9(4), e91431 (2014)

    Article  Google Scholar 

  17. Chi, Y., Song, X., Zhou, D., Hino, K., Tseng, B.L.: On evolutionary spectral clustering. ACM Trans. Knowl. Disc. Data (TKDD) 3(4), 1–30 (2009)

    Article  Google Scholar 

  18. Kim, M.-S., Han, J.: CHRONICLE: a two-stage density-based clustering algorithm for dynamic networks. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS (LNAI), vol. 5808, pp. 152–167. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04747-3_14

    Chapter  Google Scholar 

  19. 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 

  20. 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 

  21. Folino, F., Pizzuti, C.: An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans. Knowl. Data Eng. 26(8), 1838–1852 (2014)

    Article  Google Scholar 

Download references

Acknowledgement

This research was partially supported by “The Fundamental Theory and Applications of Big Data with Knowledge Engineering” under the National Key Research and Development Program of China with Grant No. 2018YFB1004500, the National Science Foundation of China under Grant Nos. 62037001, 61721002, 62050194 and 62002282, the MOE Innovation Research Team No. IRT_17R86, and Project of XJTU-SERVYOU Joint Tax-AI Lab.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

    Hanning Zhang & Boqin Feng

  2. School of Continuing Education, Xi’an Jiaotong University, Xi’an, 710049, China

    Bo Dong

  3. National Engineering Lab for Big Data Analytics, Xi’an Jiaotong University, Xi’an, 710049, China

    Bo Dong

  4. Xi’an Network Computing Data Technology Co., Ltd., Xi’an, 710049, China

    Haiyu Wu

Authors
  1. Hanning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haiyu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Boqin Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Dong .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Melbourne, VIC, Australia

    Marcello La Rosa

  2. The University of Queensland, St Lucia, QLD, Australia

    Shazia Sadiq

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Ernest Teniente

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, H., Dong, B., Wu, H., Feng, B. (2021). A Multi-label Propagation Community Detection Algorithm for Dynamic Complex Networks. In: La Rosa, M., Sadiq, S., Teniente, E. (eds) Advanced Information Systems Engineering. CAiSE 2021. Lecture Notes in Computer Science(), vol 12751. Springer, Cham. https://doi.org/10.1007/978-3-030-79382-1_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79382-1_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79381-4

  • Online ISBN: 978-3-030-79382-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation