Unsupervised Link Prediction Based on Time Frames in Weighted–Directed Citation Networks

Link prediction is one of the key rolling issues in the analysis of weighted and directed social network’s evolution according to the temporal information. It 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 undirected 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 try 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, a time frame based score is proposed for pairs of nodes in different frames of time in the network. Experiments implemented by using unsupervised learning strategy on a weighted–directed citation network show that the proposed method finds satisfactory results and are promising.

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