Co-evolutionary dynamics in social networks: A case study of Twitter

Abstract

Complex networks often exhibit co-evolutionary dynamics, meaning that the network topology and the state of nodes or links are coupled, affecting each other in overlapping time scales. We focus on the co-evolutionary dynamics of online social networks, and on Twitter in particular. Monitoring the activity of thousands of Twitter users in real-time, and tracking their followers and tweets/retweets, we propose a method to infer new retweet-driven follower relations. The formation of such relations is much more likely than the exogenous creation of new followers in the absence of any retweets. We identify the most significant factors (reciprocity and the number of retweets that a potential new follower receives) and propose a simple probabilistic model of this effect. We also discuss the implications of such co-evolutionary dynamics on the topology and function of a social network.

11 pages, 7 figures

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• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
• The Bursty Dynamics of the Twitter Information Network
• A hierarchical model of non-homogeneous Poisson processes for Twitter retweets
• On the Efficiency of the Information Networks in Social Media
• Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization
• Detecting and modelling real percolation and phase transitions of information on social media