A double attention graph network for link prediction on temporal graph

Abstract

Many data in the real world can be abstracted into the format of dynamic graph, and predicting the connection relationship between nodes in graph is a problem that is often faced in the researching. With the development of deep learning techniques, Graph Neural Networks(GNNs) have been widely used to solve graph-related problems and achieved great results. However, most of the works focus on static graph, or applied the method of static graph to coarse grained discrete time graph after slice dynamic graph into snapshot. As a kind of data that incorporates all the time process information, temporal graph contains a variety of information that worth to be explored. Several works have been done for temporal graph data, but there are still shortcomings. We believe that when researching the evolution of dynamic graph, the influence of the surrounding environment on each node in local time and space is decisive for the properties of the node, which has not been considered in the previous works. Therefore, we propose a novel general model: Double Attention Temporal Graph Network(DATGN). Through an activity based sampling algorithm, the significant nodes in the local time-structure space that associated with each node are sampled and generated as sequence, then the global information of node is aggregated through a global attention network. After which the information of the sampled sequences is aggregated by a local attention network that does not depend on edge relations, and the final representation vector of the node is obtained to make the prediction. In the experiment, we selected three data sets from different knowledge domains and compared them with the current state-of-art models. In the transductive and inductive link prediction tasks, DATGN both achieved the best results in terms of accuracy and operational efficiency than other baseline models, and we discussed the reasons of this improvement. The experiment results demonstrate that the local spatial–temporal network layer can capture the evolutionary pattern of the time sequence and improve the accuracy of link prediction.