Advertisement

CDIA: A Feasible Community Detection Algorithm Based on Influential Nodes in Complex Networks

  • Xinyu Huang
  • Dongming ChenEmail author
  • Tao Ren
  • Dongqi Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Community detection is a fundamental research in network science, which has attracted researchers all over the world to devoting into this work. However, the existing algorithms can hardly hold performance and efficiency simultaneously. Aiming at addressing the problem, and inspired by the force in physics, this paper defines the node influence from a network perspective. Afterwards, a novel approach to detect communities in terms of influential nodes is proposed. Furthermore, the vital nodes and overlapping nodes can be obtained. Series of experiments on synthetic and real-world networks are conducted, and the experimental results show that the proposed algorithm is capable and effective, which provides a reliable solution for analyzing network structure in-depth.

Keywords

Influential nodes Nodes gravity Complex network Community detection Overlapping nodes 

Notes

Acknowledgements

This work is partially supported by Liaoning Natural Science Foundation under Grant No. 20170540320, the Doctoral Scientific Research Foundation of Liaoning Province under Grant No. 20170520358, the National Natural Science Foundation of China under Grant No. 61473073, the Fundamental Research Funds for the Central Universities under Grant No. N161702001, No. N172410005-2.

References

  1. 1.
    Newman, M.: Networks. An introduction. Astron. Nachr. 327(8), 741–743 (2010)Google Scholar
  2. 2.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)CrossRefGoogle Scholar
  6. 6.
    Zhu, J., Wang, B., Wu, B., Zhang, W.: Emotional community detection in social network. IEICE Trans. Inf. Syst. 100(10), 2515–2525 (2017)CrossRefGoogle Scholar
  7. 7.
    Chao-Yi, L.I., Zhang, Y.S., Tong, L.L.: A micro-blog personalized recommendation algorithm based on community discovery. Microelectron. Comput. 34(6), 40–44 (2017)Google Scholar
  8. 8.
    Wang, D., Li, J., Xu, K., Wu, Y.: Sentiment community detection: exploring sentiments and relationships in social networks. Electron. Commer. Res. 17(1), 103–132 (2017)CrossRefGoogle Scholar
  9. 9.
    Rossetti, G., Cazabet, R.: Community discovery in dynamic networks: a survey. ACM Comput. Surv. 51(2), 35 (2017)Google Scholar
  10. 10.
    Blondel, V.D., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), 10008 (2008)CrossRefGoogle Scholar
  11. 11.
    Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: a structural clustering algorithm for networks. In: KDD, pp. 824–833 (2007)Google Scholar
  12. 12.
    Palla, G., Derenyi, I., Farkas, I.J., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005) CrossRefGoogle Scholar
  13. 13.
    Lei, Z., Pan, H., Su, Y., Zhang, X., Niu, Y.: A mixed representation-based multiobjective evolutionary algorithm for overlapping community detection. IEEE Trans. Cybern. PP(99), 1–14 (2017)Google Scholar
  14. 14.
    Chen, D.M., Sima, D.F., Huang, X.Y.: Overlapping community and node discovery algorithm based on edge similarity, no. iceit (2017)Google Scholar
  15. 15.
    Meghanathan, N.: A greedy algorithm for neighborhood overlap-based community detection. Algorithms 9(1), 8 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35 (1977)CrossRefGoogle Scholar
  17. 17.
    Pan, Y., Tan, W., Chen, Y.: The analysis of key nodes in complex social networks (2017)Google Scholar
  18. 18.
    Danon, L., Dazguilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech. 2005(09), 09008 (2005)CrossRefGoogle Scholar
  19. 19.
    Sehgal, U., Kaur, K., Kumar, P.: Notice of violation of IEEE publication principles - the anatomy of a large-scale hyper textual web search engine. In: 2009 Second International Conference on Computer and Electrical Engineering, Dubai, pp. 491–495 (2009)Google Scholar
  20. 20.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)CrossRefGoogle Scholar
  21. 21.
    Li, Z., Ren, T., Ma, X., Liu, S., Zhang, Y., Zhou, T.: Identifying influential spreaders by gravity model. Sci. Rep. 9(1), 8387 (2019)CrossRefGoogle Scholar
  22. 22.
    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)CrossRefGoogle Scholar
  23. 23.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  24. 24.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)CrossRefGoogle Scholar
  25. 25.
    Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Newman, M.E.J., Leicht, E.A.: Mixture models and exploratory analysis in networks. Proc. Natl. Acad. Sci. 104(23), 9564–9569 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xinyu Huang
    • 1
  • Dongming Chen
    • 1
    Email author
  • Tao Ren
    • 1
  • Dongqi Wang
    • 1
  1. 1.Software CollegeNortheastern UniversityShenyangChina

Personalised recommendations