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. Finally, we briefly consider a second instance of co-evolutionary dynamics on Twitter, namely the possibility that a user removes a follower link after receiving a tweet or retweet from the corresponding followee.

Epidemic processes over adaptive state-dependent networks

“…As a consequence of social distancing, the structure of the network adapts to the dynamics of the epidemics taking place in the network. Similar adaptation mechanisms have been studied in the context of power networks [14], biological and neural networks [15,16] and on-line social networks [17].…”

A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets

“…Therefore efforts have been made to model the dynamics of information diffusion and network evolution simultaneously, as it is natural to conjecture that they co-evolve over time. Antoniades and Dovrolis (2015) propose a tweet-retweet-follow model, which is characterised by events of a follower of a retweeter becoming also a follower of the original tweet author, conditional on the original tweet being created and retweeted. Farajtabar et al (2015) consider the follower and the retweet adjacency matrices, and model the co-evolution through a system of dynamic equations of these two matrices.…”

Cloud-based architectures for geo-located blogosphere dynamics detection

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