Impact of network density on cascade size and community growth

Abstract

This paper addresses two critical questions related to (1) the co-evolutionary dynamics between information diffusion and network topology and (2) the relationship between viral content and social reinforcement. A recurrence relation model is developed to formulate the growth dynamics of a community centered on a dominant user using the tweet-retweet-follow (TRF) and exogenous link-creation events. This model illustrates several fundamental relations among parameters and quantities that are critical to answering the questions. The model reproduces social reinforcement and structural trapping effects, including empirical evidence that viral content does not require strong social reinforcement. In addition, the model demonstrates that the community growth rate is influenced not only by the rate of retweets but also by the network density of the community.