Information Diffusion in Complex Networks: The Active/Passive Conundrum

  • Letizia MilliEmail author
  • Giulio Rossetti
  • Dino Pedreschi
  • Fosca Giannotti
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)


Ideas, information, viruses: all of them, with their mechanisms, can spread over the complex social tissues described by our interpersonal relations. Classical spreading models can agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such simplification makes easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, partial, simulation outcomes. In this work we discuss the concepts of active and passive diffusion: moving from analysis of a well-known passive model, the Threshold one, we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation. Our data-driven analysis shows how, in such context, the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches.



This work is funded by the European Community’s H2020 Program under the funding scheme. “FETPROACT-1-2014: Global Systems Science (GSS)”, grant agreement # 641191 CIMPLEX “Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories”(CIMPLEX: This work is supported by the European Community’s H2020 Program under the scheme “INFRAIA-1-2014-2015: Research Infrastructures”, grant agreement #654024 “SoBigData: Social Mining & Big Data Ecosystem”(SoBigData:


  1. 1.
    Aral, S., Muchnik, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Natl. Acad. Sci. 106(51), 21544–21549 (2009)CrossRefGoogle Scholar
  2. 2.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: 2th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54. ACM (2006)Google Scholar
  3. 3.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: 21st International Conference on World Wide Web, pp. 519–528. ACM (2012)Google Scholar
  4. 4.
    Burt, R.S.: Social contagion and innovation: cohesion versus structural equivalence. Am. J. Sociology (1987)Google Scholar
  5. 5.
    Centola, D.: The spread of behavior in an online social network experiment. Science 329(5996), 1194–1197 (2010)CrossRefGoogle Scholar
  6. 6.
    Centola, D.: An experimental study of homophily in the adoption of health behavior. Science 334(6060), 1269–1272 (2011)CrossRefGoogle Scholar
  7. 7.
    Gleeson, J.P., Cahalane, D.J.: Seed size strongly affects cascades on random networks. Phys. Rev. E 75(5), 056103 (2007)Google Scholar
  8. 8.
    Granovetter, M.: Threshold models of collective behavior. Am. J. Sociology (1978)Google Scholar
  9. 9.
    Havlin, S.: Phone infections. Science (2009)Google Scholar
  10. 10.
    Pennacchioli, D., Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F., Coscia, M.: The three dimensions of social prominence. In: International Conference on Social Informatics, pp. 319–332. Springer, Cham (2013)Google Scholar
  11. 11.
    Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)Google Scholar
  12. 12.
    Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: 20th International Conference on World Wide Web, pp. 695–704. ACM (2011)Google Scholar
  13. 13.
    Rossetti, G., Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., Giannotti, F.: NDlib: Studying network diffusion dynamics. Accepted at DSAA, Tokyo (2017)Google Scholar
  14. 14.
    Ruan, Z., Iniguez, G., Karsai, M., Kertész, J.: Kinetics of social contagion. Phys. Rev. Lett. 115(21), 218702 (2015)Google Scholar
  15. 15.
    Singh, P., Sreenivasan, S., Szymanski, B.K., Korniss, G.: Threshold-limited spreading in social networks with multiple initiators. arXiv:1304.7034 (2013)
  16. 16.
    Suri, S., Watts, D.J.: Cooperation and contagion in web-based, networked public goods experiments. PloS one 6(3), e16836 (2011)Google Scholar
  17. 17.
    Szor, P.: Fighting computer virus attacks. USENIX (2004)Google Scholar
  18. 18.
    Toole, J.L., Cha, M., González, M.C.: Modeling the adoption of innovations in the presence of geographic and media influences. PloS one 7(1), e29528 (2012)Google Scholar
  19. 19.
    Wang, P., González, M.C., Menezes, R., Barabási, A.L.: Understanding the spread of malicious mobile-phone programs and their damage potential. Int. J. Inf. Secur. (2013)Google Scholar
  20. 20.
    Watts, D.J.: A simple model of global cascades on random networks. Natl. Acad. Sci. 99(9), 5766–5771 (2002)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Letizia Milli
    • 1
    • 2
    Email author
  • Giulio Rossetti
    • 2
  • Dino Pedreschi
    • 1
    • 2
  • Fosca Giannotti
    • 2
  1. 1.University of PisaPisaItaly
  2. 2.KDD Lab.ISTI-CNRPisaItaly

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