INFERRING SOCIAL INFLUENCE IN DYNAMIC NETWORKS

One interesting problem in social network analysis is whether individuals’ behaviors or opinions can spread from one to another, which is known as social influence. The degrees of influence describes how far the influence can pass through individuals. In this paper, we explore the degrees of influence in dynamic networks. We build a longitudinal influence model to specify how people’s behaviors can be influenced by others in a dynamic network. In order to determine the degrees of influence, we propose a sequential hypothesis testing procedure and use generalized estimating equations to account for multiple observations of the same individual across different time points. In addition, we show that the power of our proposed test goes to one as the network size goes to infinity. We illustrate the performance of our proposed method in simulation studies and real data analysis.