Advertisement

From Complex Network to Skeleton: \( \varvec{m}_{\varvec{j}} \)-Modified Topology Potential for Node Importance Identification

  • Hanning Yuan
  • Kanokwan MalangEmail author
  • Yuanyuan Lv
  • Aniwat Phaphuangwittayakul
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

Node importance identification is a crucial content in studying the substantial information and the inherent behaviors of complex network. On the basis of topological characteristics of nodes in complex network, we introduce the idea of topology potential from data field theory to capture the important nodes and view it as the skeleton nodes. Inspired by an assumption that different mass of node (\( m_{j} \) parameter) reflects different quality and interaction reliability over the network space. We propose TP-KS method that is an improved topology potential algorithm whose \( m_{j} \) is identified by k-shell centrality. The important nodes identified by TP-KS is ranked and verified by SIR epidemic spreading model. Through the theoretical and experimental analysis, it is proved that TP-KS can effectively extract the importance of nodes in complex network. The better results from TP-KS are also confirmed in both real-world networks and artificial random scale-free networks.

Keywords

Complex network Skeleton network Topology potential Node importance evaluation 

Notes

Acknowledgement

This work was supported by the National Key Research and Development Program of China (No. 2016YFB0502600), The National Natural Science Fund of China (61472039), Beijing Institute of Technology International Cooperation Project (GZ2016085103), and Open Fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (2017NGCMZD03).

References

  1. 1.
    Li, D., Wang, S., Li, D.: Spatial Data Mining. Theory and Application. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-48538-5CrossRefGoogle Scholar
  2. 2.
    Grady, D., Thiemann, C., Brockmann, D.: Robust classification of salient links in complex networks. Nat. Commun. 3(1), 864 (2012)CrossRefGoogle Scholar
  3. 3.
    Kumari, T., Gupta, A., Dixit, A.: Comparative study of page rank and weighted page rank algorithm, vol. 2, no. 2, p. 9 (2007)Google Scholar
  4. 4.
    Zhang, D., Gao, L.: Virtual network mapping through locality-aware topological potential and influence node ranking. Chin. J. Electron. 23(1), 61–64 (2014)Google Scholar
  5. 5.
    Wang, Y., Yang, J., Zhang, J., Zhang, J., Song, H., Li, Z.: A method of social network node preference evaluation based on the topology potential, pp. 223–230 (2015)Google Scholar
  6. 6.
    Sun, R., Luo, W.: Using topological potential method to evaluate node importance in public opinion. In: Presented at the 2017 International Conference on Electronic Industry and Automation, EIA 2017 (2017)Google Scholar
  7. 7.
    Han, Q., Wen, H., Ren, M., Wu, B., Li, S.: A topological potential weighted community-based recommendation trust model for P2P networks. Peer-Peer Netw. Appl. 8(6), 1048–1058 (2015)CrossRefGoogle Scholar
  8. 8.
    Lei, X., Zhang, Y., Cheng, S., Wu, F.-X., Pedrycz, W.: Topology potential based seed-growth method to identify protein complexes on dynamic PPI data. Inf. Sci. 425, 140–153 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ding, X., Wang, Z., Chen, S., Huang, Y.: Community-based collaborative filtering recommendation algorithm. Int. J. Hybrid Inf. Technol. 8(2), 149–158 (2015)CrossRefGoogle Scholar
  10. 10.
    Han, Q., et al.: A P2P recommended trust nodes selection algorithm based on topological potential. In: 2013 IEEE Conference on Communications and Network Security, CNS, pp. 395–396 (2013)Google Scholar
  11. 11.
    Wang, Z., Zhao, Y., Chen, Z., Niu, Q.: An improved topology-potential-based community detection algorithm for complex network. Sci. World J. 2014, 1–7 (2014)Google Scholar
  12. 12.
    Wang, S., Gan, W., Li, D., Li, D.: Data field for hierarchical clustering. Int. J. Data Warehouse. Min. 7(4), 43–63 (2011)CrossRefGoogle Scholar
  13. 13.
    Han, Y., Li, D., Wang, T.: Identifying different community members in complex networks based on topology potential. Front. Comput. Sci. China 5(1), 87–99 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Xiao, L., Wang, S., Li, J.: Discovering community membership in biological networks with node topology potential. In: 2012 IEEE International Conference on Granular Computing, GrC, pp. 541–546 (2012)Google Scholar
  15. 15.
    Kitsak, M., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)CrossRefGoogle Scholar
  16. 16.
    Network data. http://www-personal.umich.edu/~mejn/netdata/. Accessed 14 May 2018
  17. 17.
    Alex Arenas datasets. http://deim.urv.cat/~alexandre.arenas/data/welcome.htm. Accessed 14 May 2018
  18. 18.
    Tang, Y., Li, M., Wang, J., Pan, Y., Wu, F.-X.: CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 127, 67–72 (2015)CrossRefGoogle Scholar
  19. 19.
    Lawyer, G.: Understanding the influence of all nodes in a network. Scientific reports, vol. 5, no. 1, August 2015Google Scholar
  20. 20.
    Kendall, M.G.: THE treatment of ties in ranking problems. Biometrika 33(3), 239–251 (1945)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ma, L.-L., Ma, C., Zhang, H.-F., Wang, B.-H.: Identifying influential spreaders in complex networks based on gravity formula. Phys. Stat. Mech. Appl. 451, 205–212 (2016)CrossRefGoogle Scholar
  22. 22.
    Liu, J., Xiong, Q., Shi, W., Shi, X., Wang, K.: Evaluating the importance of nodes in complex networks. Phys. Stat. Mech. Appl. 452, 209–219 (2016)CrossRefGoogle Scholar
  23. 23.
    Wang, J., Li, M., Wang, H., Pan, Y.: Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(4), 1070–1080 (2012)CrossRefGoogle Scholar
  24. 24.
    Li, M., Wang, J., Chen, X., Wang, H., Pan, Y.: A local average connectivity-based method for identifying essential proteins from the network level. Comput. Biol. Chem. 35(3), 143–150 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Anthonisse, J.M.: The rush in a directed graph, January 1971Google Scholar
  26. 26.
    Sabidussi, G.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Tang, L., Liu, H.: Community detection and mining in social media. Synth. Lect. Data Min. Knowl. Discov. 2(1), 1–137 (2010)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Bonacich, P.: Power and centrality: a family of measures. Am. J. Sociol. 92(5), 1170–1182 (1987)CrossRefGoogle Scholar
  29. 29.
    Estrada, E., Hatano, N.: Resistance distance, information centrality, node vulnerability and vibrations in complex networks. In: Estrada, E., Fox, M., Higham, D.J., Oppo, G.-L. (eds.) Network Science, pp. 13–29. Springer, London, London (2010).  https://doi.org/10.1007/978-1-84996-396-1_2CrossRefGoogle Scholar
  30. 30.
    Estrada, E., Rodríguez-Velázquez, J.A.: Subgraph centrality in complex networks. Phys. Rev. E 71(5), 056103 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hanning Yuan
    • 1
  • Kanokwan Malang
    • 1
    Email author
  • Yuanyuan Lv
    • 1
  • Aniwat Phaphuangwittayakul
    • 2
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingPeople’s Republic of China
  2. 2.International College of Digital InnovationChiang Mai UniversityChiang MaiThailand

Personalised recommendations