Multivariate Temporal Link Prediction in Evolving Social Networks

Link prediction in social networks refers to predicting the emergence of future connections between nodes. It is considered as one of the important tasks in various data mining applications for recommendation systems, bioinformatics, world wide web and it has attracted a great deal of attention recently. There are several studies on link prediction based on static topological similarity metrics and static graph representation without considering the temporal evolutions of link occurrences. Most of the previous methods for link prediction in evolving networks use the exisiting connections in the network to predict new ones. In this paper, we propose a novel method, called Multivariate Time Series Link Prediction, for link prediction in evolving networks that integrates (1) temporal evolution of the network; (2) node similarities; (3) node connectivity information. The proposed method is based on a Vector Autoregression (VAR) Model for Multivariate Time Series forecasting which enables to represent time information over a combination of node similarities and node connectivities. The proposed method is tested on coauthorship networks. It is shown that integrating time information with node similarities and node connectivities improves the link prediction performance to a large extent.