PREDICTING LINKS AND LINK CHANGE IN FRIENDS NETWORKS: SUPERVISED TIME SERIES LEARNING WITH IMBALANCED DATA

摘要

We address the problem of predicting links and link change in friends networks and introduce a new supervised learning method for both types of prediction. This extends previous based on directed graph features such as the indegree of candidate friends and pair dependent relational features such as common interests. In this new work, we consider how differential user data, such as that produced using regular crawls from a social network site, can be used to produce a time series with which we can identify prediction problems over both links and link change. A key issue we address is the rarity of change between two successive versions of a social network, resulting in severe imbalance between positive and negative examples of change. We compare existing approaches towards coping with this problem, present positive results on new crawls of LiveJoumal, and consider how temporal data can enhance the relational link mining process.