Deep graph tensor learning for temporal link prediction

Knowledge discovery on dynamic graphs has received much attention in recent years. As a key task of dynamic graph research, the goal of temporal link prediction is to accurately predict the time-varying links in dynamic networks. Uncertainty in link emergence is a major challenge in this research, as it is not easy to learn stable and reliable link-level feature representations, which are usually readily available on static graphs. In order to adapt to the ever-changing graph structure, this paper proposes to construct a deep graph tensor learning model, which can capture the contextual characteristics of graph evolution from both the graph structure (spatial) mode and the link sequence (temporal) mode. Therefore, compared to link prediction on static graphs, temporal link prediction can benefit more from the link-level embedding representations coupled with spatio-temporal features. The experimental results on seven public dynamic graph datasets show that the prediction accuracy obtained by the new model is overall better than competing models such as GC-LSTM, EvolveGCN, and HTGN. In the meantime, as a result of getting rid of the traditional RNN learning paradigm, the new model is also significantly better than the traditional temporal graph learning model in terms of training efficiency.