Use of Supervised Learning to Predict Directionality of Links in a Network
نویسندگان
چکیده
Often, the information contained in network data is incomplete. Many avenues of research are aimed at addressing this incompleteness. For example, the link prediction problem attempts to identify which missing links are most likely to exist in the complete network. In this paper, we consider a related, but different, problem: predicting the directions of links in a directed network. We treat this problem as a supervised learning problem in which the directions of some edges are known. We calculate various features of each known edge based on its position in the network, and use a Support Vector Machine to predict the unknown directions of edges. We consider four networks, and show that in each case, this method performs significantly better than other compared methods.
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