Analysis of Link Prediction Methods in Weighted and Unweighted Citation Network

Networks can be used to represent a variety of real world complex interacting systems in which vertices represents interacting entities and a network link represents a connection between two nodes or entities. Citation graphs are widely utilized in a variety of graph mining situations like citation recommendation and locating research hotspots. Link prediction is considered as a significant task in data and graph mining and deals with prediction of the future or missing network links based on the given network knowledge. In this research, the problem of prediction of links in weighted citation network is addressed and also we compare how much weighing the network can improve the link prediction accuracy. Normally link prediction problems consider only the existence of links. This might lead to a less accurate prediction as it will not give the strength of the relationship between the two entities. In this study, we analyzed the Search Path Count method, which is used to assign weights to the citation links. So rather than just considering the presence of the links, two weighted path methods using Search Path Count weights are proposed in this research for link prediction. Experiments on real citation dataset show that using the Search Path Count weights to evaluate the relevance of the edges in citation networks improves the accuracy of link prediction systems.