PREDICTION OF NEW CITATION LINKS IN AUTHOR-AUTHOR DIRECTED NETWORK

Abstract

Link prediction in weighted and directed networks according to the application of temporal information can be point as an important problem in social network analysis. Link prediction tends to guess the likelihood of the connections occurrence between nodes. In addition the link prediction aims to determine the missing links in the network, which uses the state of the network up to a given time for predicting the new links in future. Most of the previous works have deployed to unweighted or un-directed networks and for computing the proximity scores, only the current state of the network has considered without taking any temporal information into account, which can be point as a limitation in link prediction studies. In this study we tried to overcome the above mentioned limitation by analyzing the development of topological measures in a weighted-directed citation network on a specific period of time. For achieving this aim, chosen similarity metric deployed to all non-connected pairs of nodes in different frames of time in the network. Then, time frames are built for each pair to record their values which provided by the metric. Experiments on unsupervised prediction on a weighted-directed citation network show that the proposed method finds satisfactory results and is promising.