Process-driven Analysis of Dynamics in Online Social Interactions

Measurement studies of online social networks 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 ex-amine detailed interaction data over time, and studied the problem of modeling user interactions. A generative mod-el can shed light on the fundamental processes that underlie user interactions. In this paper, we analyze the first complete record of full interaction and network dynamics in a large online social network. Our dataset covers all wall posts, new user events, and new social link events during the first full year of Ren-ren, the largest social network in China, including 623K new users, 8.2 million new links, and 29 million wall posts. Our analysis provides surprising insights into the evolution of user interactions over time. We find that users invite new friends to interact at a nearly constant rate, prefer to inter-act with friends with whom they share significant overlaps in social circles, and most social links drop in interaction frequency over time. We also validate our findings on Face-book, and show that they do generalize across OSNs. We use our insights to derive a generative model of so-cial interactions that accurately captures both our new results and previously observed network properties. Our model cap-tures the inherently heterogeneous strengths of social links, and has broad implications on the design of social network algorithms such as friend recommendation, information dif-fusion and viral marketing. 1