Practical Social Network Analysis with Python pp 145-172 | Cite as

# Cascading in Social Networks

## Abstract

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.

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