skip to main content
research-article
Free Access

Even central users do not always drive information diffusion

Authors Info & Claims
Published:28 January 2019Publication History
Skip Abstract Section

Abstract

Diffusion speed and scale depend on all kinds of information, not just which users have the most or fewest connections.

References

  1. Adamic, L.A. and Glance, N. The political blogosphere and the 2004 U.S. election: Divided they blog. In Proceedings of the Third International Workshop on Link Discovery (Chicago, IL, Aug. 21--25). ACM Press, New York, 2005, 36--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Albert, R. and Barabási, A.-L. Statistical mechanics of complex networks. Reviews of Modern Physics 74, 1 (Jan. 2002), 47--97.Google ScholarGoogle ScholarCross RefCross Ref
  3. Borgatti, S.P. Centrality and network flow. Social Networks 27, 1 (Jan. 2005), 55--71.Google ScholarGoogle ScholarCross RefCross Ref
  4. De Meo, P., Ferrara, E., Fiumara, G., and Provetti, A. On Facebook, most ties are weak. Commun. ACM 57, 11 (Oct. 2014), 78--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Doerr, B., Fouz, M., and Friedrich, T. Why rumors spread so quickly in social networks. Commun. ACM 55, 6 (June 2012), 70--75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Gao, C. and Liu, J.M. Network-based modeling for characterizing human collective behaviors during extreme events. IEEE Transactions on System, Man, and Cybernetics: Systems 47, 1 (Jan. 2017), 171--183.Google ScholarGoogle Scholar
  7. Gao, C., and Liu, J.M. Modeling and restraining mobile virus propagation. IEEE Transactions on Mobile Computing 12, 3 (Mar. 2013), 529--541. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8 Goel, S., Watts, D.J., and Goldstein, D.G. The structure of online diffusion networks. In Proceedings of the 13<sup>th</sup> ACM Conference on Electronic Commerce (Valencia, Spain, June 4--8). ACM Press, New York, 2012, 623--638. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Guimerá, R., Danon, L., Díaz-Guilera, A., Giralt, F., and Arenas, A. Self-similar community structure in a network of human interactions. Physical Review E 68, 6 (Dec. 2003), 065103.Google ScholarGoogle ScholarCross RefCross Ref
  10. Howard, B. Analyzing online social networks. Commun. ACM 51, 11 (Nov. 2008), 14--16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., and Makse, H.A. Identification of influential spreaders in complex networks. Nature Physics 6, 11 (Aug. 2010), 888--893.Google ScholarGoogle ScholarCross RefCross Ref
  12. Lancichinetti, A., Fortunato, S., and Radicchi, F. Benchmark graphs for testing community detection algorithms. Physical Review E 78, 4 (Oct. 2008).Google ScholarGoogle ScholarCross RefCross Ref
  13. Leskovec, J., Kleinberg, J., and Faloutsos, C. Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data 1, 1 (Mar. 2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Liu, Y.Y., Slotine, J.J., and Barabási, A.-L. Controllability of complex networks. Nature 473, 7346 (May 2011), 167--173.Google ScholarGoogle ScholarCross RefCross Ref
  15. McGoogan, C. What is WannaCry and how does ransomware work? The Telegraph (May 18, 2017); http://www.telegraph.co.uk/technology/0/ransomware-does-work/Google ScholarGoogle Scholar
  16. Nematzadeh, A., Ferrara, E., Flammini, A., and Ahn, Y.-Y. Optimal network modularity for information diffusion. Physical Review Letters 113, 8 (Aug. 2014), 088701.Google ScholarGoogle Scholar
  17. Newman, M.E.J. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 23 (June 2006), 8577--8582.Google ScholarGoogle ScholarCross RefCross Ref
  18. Newman, M.E.J. Co-authorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences 101, Supplement 1 (Apr. 2004), 5200--5205.Google ScholarGoogle ScholarCross RefCross Ref
  19. Ranjbar, A. and Maheswaran, M. Using community structure to control information sharing in online social networks. Computer Communications 41 (Jan. 2014), 11--21. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Wang, W., Tang, M., Stanley, H.E., and Braunstein, L.A. Unification of theoretical approaches for epidemic spreading on complex networks. Reports on Progress in Physics 80, 3 (Feb. 2017), 036603.Google ScholarGoogle ScholarCross RefCross Ref
  21. Xie, J.R., Kelley, S., and Szymanski, B.K. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys 45, 4 (Aug. 2013), 43:1--43:35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Zou, C.C., Towsley D., and Gong W. Modeling and simulation study of the propagation and defense of Internet e-mail worms. IEEE Transactions on Dependable and Secure Computing 4, 2 (Apr. 2007), 105--118. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Even central users do not always drive information diffusion

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image Communications of the ACM
          Communications of the ACM  Volume 62, Issue 2
          February 2019
          112 pages
          ISSN:0001-0782
          EISSN:1557-7317
          DOI:10.1145/3310134
          Issue’s Table of Contents

          Copyright © 2019 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 28 January 2019

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Popular
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format