Dynamic Link Prediction by Integrating Node Vector Evolution and Local Neighborhood Representation

Abstract

Many networks in real applications are constantly evolving as the creation and elimination of nodes and edges. Dynamic link prediction aims to infer whether there will be an edge between a pair of nodes, given the recent evolution history of the network. In this paper, we devise a flexible framework for link prediction on dynamic networks regularly archived as different snapshots. On the basis of node vectors learned on individual snapshots, a gated recurrent unit (GRU) network is utilized to model the node vector evolution series and predict the node representation in the future. Then, the edge representation is not only constructed from the interaction between representations of the target node pair, but also enriched with local neighborhood representations---historical embeddings of their common neighbors. Finally, a binary classifier is trained to perform link prediction. The framework can be instantiated with many off-the-shelf outstanding node embedding and binary classification methods. Extensive experiments on three different datasets demonstrate the effectiveness and flexibility of our proposed framework. Ablation studies show that the node vector evolution and local neighborhood representation both have positive but different effects on dynamic link prediction on diverse networks.