A Novel Method of Influence Ranking via Node Degree and H-index for Community Detection

  • Qiang LiuEmail author
  • Lu Deng
  • Junxing Zhu
  • Fenglan Li
  • Bin Zhou
  • Peng Zou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)


Identifying influential nodes is critical to have a better understanding of the network function and the process of information diffusion. Traditional methods of evaluating influential nodes such as degree centrality ignore the location of a node and its neighbors’ influence in networks, while this plays an important role in revealing the node’s local influence in spreading information. In this paper, we propose a novel method, named DH-index (node Degree and H-index), to measure a node’ importance by considering its and neighbors’ influence simultaneously. Meanwhile, we put forward a node DH-index based label propagation algorithm (DH_LPA) for community detection. We demonstrate its validity and feasibility on a set of real-world and synthetic networks for our new proposed community detection method.


Influence ranking Information diffusion Community detection Label propagation 



This work was supported by 973 Program of China (Grant No. 2013CB329601, 2013CB329604, 2013CB329606).


  1. 1.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle 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)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)CrossRefGoogle Scholar
  4. 4.
    Xu, B., Deng, L., Jia, Y., Zhou, B., Han, Y.: Overlapping community detection on dynamic social network. In: Proceedings of the 6th International Symposium on Computational Intelligence and Design, Hangzhou, China, vol. 2, pp. 321–326 (2013)Google Scholar
  5. 5.
    Liu, Q., Zhou, B., Li, S., Li, A.P., Zou, P., Jia, Y.: Community detection utilizing a novel multi-swarm fruit fly optimization algorithm with hill-climbing strategy. Arab. J. Sci. Eng. 41, 807–828 (2016)CrossRefGoogle Scholar
  6. 6.
    Lin, W., Kong, X., Yu, P.S., Wu, Q., Jia, Y., Li, C.: Community detection in incomplete information networks. In: 21st International Conference on World Wide Web, pp. 341–350. ACM Press (2012)Google Scholar
  7. 7.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)CrossRefGoogle Scholar
  8. 8.
    Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints. Phys. Rev. E 80, 026129 (2009)CrossRefGoogle Scholar
  9. 9.
    Leung, I.X., Hui, P., Lio, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E 79, 066107 (2009)CrossRefGoogle Scholar
  10. 10.
    Subelj, L., Bajec, M.: Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys. Rev. E 83, 036103 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    He, M., Leng, M., Li, F., Yao, Y., Chen, X.: A node importance based label propagation approach for community detection. In: Sun, F., Li, T., Li, H. (eds.) ISKE2012. Advances in Intelligent Systems and Computing, vol. 214, pp. 249–257. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Sun, H., Liu, J., Huang, J., Wang, G., Yang, Z., Song, Q., Jia, X.: CenLP: a centrality-based label propagation algorithm for community detection in networks. Phys. A Stat. Mech. Appl. 436, 767–780 (2015)CrossRefGoogle Scholar
  13. 13.
    Wang, W., Street, W.N.: A novel algorithm for community detection and influence ranking in social networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 555–560. IEEE Press, New York, August 2014Google Scholar
  14. 14.
    Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. phys. 6, 888–893 (2010)CrossRefGoogle Scholar
  15. 15.
    Lü, L., Zhou, T., Zhang, Q.M., Stanley, H.E.: The H-index of a network node and its relation to degree and coreness. Nat. Commun. 7, 10168 (2016)CrossRefGoogle Scholar
  16. 16.
    Chen, D., Lü, L., Shang, M.S., Zhang, Y.C., Zhou, T.: Identifying influential nodes in complex networks. Phys. A Stat. Mech. Appl. 391, 1777–1787 (2012)CrossRefGoogle Scholar
  17. 17.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)CrossRefGoogle Scholar
  18. 18.
    Danon, L., Díaz-Guilera, A., Arenas, A.: The effect of size heterogeneity on community identification in complex networks. J. Stat. Mech. Theory Exp. 11, P11010 (2006)CrossRefGoogle Scholar
  19. 19.
    Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech. Theory Exp. 9, P09008 (2005)Google Scholar
  20. 20.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)CrossRefGoogle Scholar
  21. 21.
    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, 396–405 (2003)CrossRefGoogle Scholar
  22. 22.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Phys. Rep. 424, 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78, 046110 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Qiang Liu
    • 1
    Email author
  • Lu Deng
    • 1
  • Junxing Zhu
    • 1
  • Fenglan Li
    • 1
  • Bin Zhou
    • 1
    • 2
  • Peng Zou
    • 1
  1. 1.College of ComputerNational University of Defense TechnologyChangshaPeople’s Republic of China
  2. 2.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaPeople’s Republic of China

Personalised recommendations