Cascading in Social Networks
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.
- 6.Goetz, Michaela, Jure Leskovec, Mary McGlohon, and Christos Faloutsos. 2009. Modeling blog dynamics. In ICWSM.Google Scholar
- 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
- 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.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.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.Miller, Mahalia, Conal Sathi, Daniel Wiesenthal, Jure Leskovec, and Christopher Potts. 2011. Sentiment flow through hyperlink networks. In ICWSM.Google Scholar
- 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.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.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