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.Online Social Networks (OSNs), such as Twitter and Facebook, have changed how individuals interact with society, how information flows between actors, and how people influence each other. These are all complex dynamic processes that are now widely studied empirically and in large scale, thanks to the availability of data from OSNs. Most OSN studies focus on one of the following two aspects of network dynamics. Dynamics on networks refer to changes in the state of network nodes or links considering a static topology [1,2]. Dynamics of networks, on the other hand, refer to changes in the topology of a network, without explicitly modeling its underlying causes [3]. As noted by Gross and Blasius in [4], however, real OSNs typically exhibit both types of dynamics, forming an adaptive, or co-evolutionary, system in which the network topology and the state of nodes/links affect each other through a (rather poorly understood) feedback loop.Dynamic processes in OSNs, such as information diffusion or influence, are obviously affected by the underlying network topology, but they also have the power to affect that topology. For instance, users may decide to add or drop a "friendship" or "follower" relation depending on what the potential "friend" or "followee" has recently said or done in the context of that OSN. Previous empirical or modeling OSN studies often choose to ignore such co-evolutionary dynamics, mostly for simplicity, assuming a static network topology, or assuming that the topology and node/link states are decoupled and evolve in separate time scales [5].The literature on co-evolutionary dynamics has relied mostly on abstract models so far, without sufficient empirical validation. For instance, Kosma and Barrat examined how the topology of an adaptive network of interacting agents and of the agents' opinions can influence each other [6]. When agents rewire their links in a way that depends on the opinions of their neighbors, the result can be either a large number of small clusters, making global consensus difficult, or a highly connected but polarized network. Shaw and Schwartz [7] examined the effects of vaccination in static ...

Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo

“…From Figs 3 and 4 , we can easily find that the probability of follow relation created is increasing with the increase of observation number. Comparing with similar studies in Twitter, our conclusion is contrary to the conclusion in 12 , however, is same with conclusions in 13 14 15 . We consider that Zhu et al 12 only examined the relationship between how often a user v received a specific user @CARightToKnow and the average probability that v followed @CARightToKnow in return.…”

“…Zhou et al 12 found that exposing the same user multiple times does not necessarily increase the probability a new link will form, and they also proposed a visibility-based model for link prediction. Myers et al 13 , Weng et al 14 and Antoniades et al 15 also focused on the same problem, but got a different conclusion that that repeated exposure to contents posted by a user increases the probability of following that user. Farajtabar et al 16 proposed a model for simulating diffusion and network events form the co-evolutionary dynamics which can be used to predict links.…”

“…In current studies, some work has noted the above problem. Zhou et al 12 , Myers et al 13 , Weng et al 14 and Antoniades et al 15 analyzed the relation between information diffusion and link creation, and Zhou et al also proposed a visibility-based model for link prediction. Farajtabar et al 16 did not analyze the influence of information diffusion on link creation, but directly explored information diffusion process to predict links.…”

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

“…This paper is an extended version of work published in [6]. We extend our previous work by examining a second instance of co-evolutionary dynamics on Twitter, namely the possibility of an unfollow event to occur.…”

Network Redundancy and Information Diffusion: The Impacts of Information Redundancy, Similarity, and Tie Strength

“…Moreover, network structures and information diffusion could also be mutually determined. For example, individuals are inclined to follow new users who they have retweeted on Twitter (Antoniades & Dovrolis, 2015). We are uncertain about to what extent mutual causality can bias our findings.…”