Uncovering user interaction dynamics in online social networks

Abstract

Measurement studies of online social networks (OSNs) show that all social links are not equal, and the strength of each link is best characterized by the frequency of interactions between the linked users. To date, few studies have been able to examine detailed interaction data over time. In this paper, we first analyze the interaction dynamics in a large online social network. We find that users invite new friends to interact at a nearly constant rate, prefer to continue interacting with friends with whom they have a larger number of historical interactions, and most social links drop in interaction frequency over time. Then, we use our insights from the analysis to derive a generative model of social interactions that can capture fundamental processes underling user interactions.