Cascading in Social Networks

  • Krishna Raj P. M.Email author
  • Ankith Mohan
  • K. G. Srinivasa
Part of the Computer Communications and Networks book series (CCN)


Cascades are described as periods during which individuals in a population exhibit herd-like behaviour because they are making decisions based on the actions of other individuals rather than relying on their own information about the problem. We will look at the two models of cascade: decision based models and probabilistic models. In the decision based model, through a coordination game, we will look at how a few individual’s behaviours can cascade through the network to decide the norm. We will learn what the optimal strategies are when there is a playoff between two incompatible competing systems, and also when bilinguality is allowed. We will also see some studies which observes cascading in real-world networks.

While decision models looks at situations where cascade propagates due to the adoption of behaviour, probabilistic models do not require the consent of an individual and instead looks at the susceptibility of the individual to be part of the cascade. This model mainly looks at the spread of diseases. Here, we will look at various concepts related to outbreak transmission. The focus will be on the SIR, SIS and the SIRS epidemic models. Finally, the chapter looks at hashtag cascades in Twitter, cascading of recommendations and the popularity of blogs in the Blogspace.


  1. 1.
    Berger, Eli. 2001. Dynamic monopolies of constant size. Journal of Combinatorial Theory, Series B 83 (2): 191–200.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bikhchandani, Sushil, David Hirshleifer, and Ivo Welch. 1992. A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy 100 (5): 992–1026.CrossRefGoogle Scholar
  3. 3.
    Centola, Damon. 2010. The spread of behavior in an online social network experiment. Science 329 (5996): 1194–1197.CrossRefGoogle Scholar
  4. 4.
    Centola, Damon, and Michael Macy. 2007. Complex contagions and the weakness of long ties. American Journal of Sociology 113 (3): 702–734.CrossRefGoogle Scholar
  5. 5.
    Centola, Damon, Víctor M. Eguíluz, and Michael W. Macy. 2007. Cascade dynamics of complex propagation. Physica A: Statistical Mechanics and its Applications 374 (1): 449–456.CrossRefGoogle Scholar
  6. 6.
    Goetz, Michaela, Jure Leskovec, Mary McGlohon, and Christos Faloutsos. 2009. Modeling blog dynamics. In ICWSM.Google Scholar
  7. 7.
    Goyal, Amit, Francesco Bonchi, and Laks V.S. Lakshmanan. 2010. Learning influence probabilities in social networks. In Proceedings of the third ACM international conference on Web search and data mining, 241–250. ACM.Google Scholar
  8. 8.
    Granovetter, Mark. 1978. Threshold models of collective behavior. American Journal of Sociology 83 (6): 1420–1443.CrossRefGoogle Scholar
  9. 9.
    Immorlica, Nicole, Jon Kleinberg, Mohammad Mahdian, and Tom Wexler. 2007. The role of compatibility in the diffusion of technologies through social networks. In Proceedings of the 8th ACM conference on Electronic commerce, 75–83. ACM.Google Scholar
  10. 10.
    Leskovec, Jure, Mary McGlohon, Christos Faloutsos, Natalie Glance, and Matthew Hurst. 2007. Patterns of cascading behavior in large blog graphs. In Proceedings of the 2007 SIAM international conference on data mining, 551–556. SIAMGoogle Scholar
  11. 11.
    Leskovec, Jure, Ajit Singh, and Jon Kleinberg. 2006. Patterns of influence in a recommendation network. In Pacific-Asia conference on knowledge discovery and data mining, 380–389. Berlin: Springer.Google Scholar
  12. 12.
    Miller, Mahalia, Conal Sathi, Daniel Wiesenthal, Jure Leskovec, and Christopher Potts. 2011. Sentiment flow through hyperlink networks. In ICWSM.Google Scholar
  13. 13.
    Morris, Stephen. 2000. Contagion. The Review of Economic Studies 67 (1): 57–78.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Myers, Seth A., and Jure Leskovec. 2012. Clash of the contagions: Cooperation and competition in information diffusion. In 2012 IEEE 12th international conference on data mining (ICDM), 539–548. IEEE.Google Scholar
  15. 15.
    Myers, Seth A., Chenguang Zhu, and Jure Leskovec. 2012. Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 33–41. ACM.Google Scholar
  16. 16.
    Romero, Daniel M., Brendan Meeder, and Jon Kleinberg. 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, 695–704. ACM.Google Scholar
  17. 17.
    Watts, Duncan J. 2002. A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences 99 (9): 5766–5771.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Krishna Raj P. M.
    • 1
    Email author
  • Ankith Mohan
    • 1
  • K. G. Srinivasa
    • 2
  1. 1.Department of ISERamaiah Institute of TechnologyBangaloreIndia
  2. 2.Department of Information TechnologyC.B.P. Government Engineering CollegeJaffarpurIndia

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