COMPLEX NETWORKS 2017: Complex Networks & Their Applications VI pp 946-954 | Cite as

Identifying Spreading Sources and Influential Nodes of Hot Events on Social Networks

  • Nan Zhou
  • Xiu-Xiu Zhan
  • Qiang Ma
  • Song Lin
  • Jun Zhang
  • Zi-Ke Zhang
Conference paper
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

The rapid development of World Wide Web accelerates information spreading in various ways. Thanks to the emergence of multiple social platforms, some events which are not much attractive in the past can become social hot spots nowadays. In this paper, we study the information diffusion process of “IP MAN3 box office fraud”, which is widely diffused in the largest Chinese microblogging system, namely Sina Weibo, in March 2016. Based on the temporal metric we have proposed, we succeed in finding out the sources of the information, and constructing the panorama of the diffusion process. In addition, a portion of nodes that promote the diffusion are identified by using the node importance algorithms. Finally, the users with abnormal behaviors in the process of event development are identified.

Keywords

Critical nodes identification Source tracing Information diffusion 
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Notes

Acknowledgments

This work was partially supported by Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LR18A050001, LY18A050004 and LQ16F030006), Natural Science Foundation of China (Grant Nos. 61673151 and 11671241) and the EUFP7 Grant 611272 (project GROWTHCOM).

References

  1. 1.
    Wang, X., Li, X., Chen, G.: Introduction to Network Science, pp. 232–265 (2012)Google Scholar
  2. 2.
    Wu, F., Huberman, B.A., Adamic, L.A., et al.: Information flow in social groups. Phys. A Stat. Mech. Appl. 337(1), 327–335 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Zanette, D.H.: Dynamics of rumor propagation on small-world networks. Phys. Rev. E 65(4), 041908 (2002)CrossRefGoogle Scholar
  4. 4.
    Moreno, Y., Nekovee, M., Pacheco, A.F.: Dynamics of rumor spreading in complex networks. Phys. Rev. E 69(6), 066130 (2004)CrossRefGoogle Scholar
  5. 5.
    Ren, X., Lü, L.: Review of ranking methods for important node of network. Chin. Sci. Bull. 59(13), 1175–1197 (2014)CrossRefGoogle Scholar
  6. 6.
    Burt, R.S., Minor, M.J.: Applied Network Analysis: A Methodological Introduction. Sage Publications, Inc (1983)Google Scholar
  7. 7.
    Dolev, S., Elovici, Y., Puzis, R.: Routing betweenness centrality. J. ACM (JACM) 57(4), 25 (2010)Google Scholar
  8. 8.
    Cheng, X.Q., Ren, F.X., Shen, H.W., et al.: Bridgeness: a local index on edge significance in maintaining global connectivity. J. Stat. Mech. Theory Exp. 2010(10), 10011 (2010)CrossRefGoogle Scholar
  9. 9.
    Martin, T., Zhang, X., Newman, M.E.J.: Localization and centrality in networks. Phys. Rev. E 90(5), 052808 (2014)CrossRefGoogle Scholar
  10. 10.
    Yuejin Tan, W., Jun, H.D.: Node contraction method for evaluating node importance in complex network. Syst. Eng. Theory Pract. 26, 79–83 (2006)Google Scholar
  11. 11.
    Hu, H., Zhu, J.H.: Social networks, mass media and public opinions. J. Econ. Interact. Coord., 1–19 (2015)Google Scholar
  12. 12.
    Xie, J.: Rumor spread in microblogging and its avoidance mechanism. J. Hefei Univ. Soc. Sci. 29(2), 51–54 (2012)Google Scholar
  13. 13.
    Wang, H., Han, J., Deng, L., et al.: Research on rumor spread dynamics based on mobile social network. Acta Phys. Sin. 62(11), 110505–110505 (2013)Google Scholar
  14. 14.
    Bao, Y., Yi, C., Xue, Y., et al.: A new rumor propagation model and control strategy on social networks. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1472–1473. ACM (2013)Google Scholar
  15. 15.
    Zhou, D., Li, Z.: High performance web reptile: a summary of research. Comput. Sci. 36(08), 26–29 (2009)Google Scholar
  16. 16.
    Zhou, Z., Zhang, H., Xie, J.: Sina microblogging data crawler based on Python. J. Comput. Appl. 34(11), 3131–3134 (2014)Google Scholar
  17. 17.
    Zhou, L., Lin, L.: A summary of the research on crawler technology. J. Comput. Appl. 25(9), 1965–1969 (2005)Google Scholar
  18. 18.
    Li, G., Gan, T., Guang, Z.: Identifying network pushing hands based on text emotion classification. Libr. Inf. Syst. 54(8), 77–80 (2010)Google Scholar
  19. 19.
    Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2(1), 113–120 (1972)CrossRefGoogle Scholar
  20. 20.
    Chen, D.B., Gao, H., Lü, L., et al.: Identifying influential nodes in large-scale directed networks: the role of clustering. PloS One 8(10), e77455 (2013)CrossRefGoogle Scholar
  21. 21.
    Kitsak, M., Gallos, L.K., Havlin, S., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)CrossRefGoogle Scholar
  22. 22.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  23. 23.
    Zeng, A., Zhang, C.J.: Ranking spreaders by decomposing complex networks. Phys. Lett. A 377, 1031–1035 (2013)CrossRefGoogle Scholar
  24. 24.
    Liu, J.G., Ren, Z.M., Guo, Q.: Ranking the spreading influence in complex networks. Phys. A 392, 4154–4159 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Nan Zhou
    • 1
  • Xiu-Xiu Zhan
    • 2
  • Qiang Ma
    • 1
  • Song Lin
    • 1
  • Jun Zhang
    • 3
  • Zi-Ke Zhang
    • 1
  1. 1.Alibaba Research Center for Complexity SciencesHangzhou Normal UniversityHangzhouPeople’s Republic of China
  2. 2.Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands
  3. 3.Shanghai Surfing City Information S&T Co. Ltd.ShanghaiChina

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