Abstract
Online Social Network becomes the most prevalent platform for exchanging information between users, maintaining friendships online. As is well-known to us, however, some friendships even those intimate ones might vanish. Therefore, precisely modeling and predicting state of each online relationship is worthwhile in many respects. For social communication services such modeling permits new and novel online services. In addition, constructing this model might enlighten us in exploiting information spreading pattern in online social network. In this paper, we propose a model in determining a probability distribution which describes the ‘surviving time’ of each friendships by applying one commonly used method in sociology, survival analysis. We discuss a series of social explanatory variables that highly affect this probability distribution. Moreover, methods in the moving average process are devoted to determining the appropriate parameter in survival model. Furthermore, to avoid the high computational complexity in kernel learning we impose sparsity in our model. Finally, with the experiments on real data, the proposed survival model is proven to be of high accuracy, and thus of great potential for further applications.
Li Song
Professor, IEEE Senior Member
Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.