Extracting Top-k Most Influential Nodes by Activity Analysis

Can we statistically compute social influence and under-stand quantitatively to what extent people are likely to be influenced by the opinion or the decision of their friends, friends of friends, or acquaintances? An in-depth under-standing of such social influence and the diffusion process of such social influence will help us better address the ques-tion of to what extent the ’word of mouth ’ effects will take hold on social networks. Most of the existing social in-fluence models to define the influence diffusion are solely based on topological connectivity of social network nodes. In this paper, we presented an activity-base social influence model. Our experimental results show that activity-based social influence is more effective in understanding the viral marketing effects on social networks.