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Temporal link prediction, as one of the most crucial work in temporal graphs,has attracted lots of attention from the research area. The WSDM Cup 2022 seeksfor solutions that predict the existence probabilities of edges within timespans over temporal graph. This paper introduces the solution of AntGraph,which wins the 1st place in the competition. We first analysis the theoreticalupper-bound of the performance by removing temporal information, which impliesthat only structure and attribute information on the graph could achieve greatperformance. Based on this hypothesis, then we introduce several well-designedfeatures. Finally, experiments conducted on the competition datasets show thesuperiority of our proposal, which achieved AUC score of 0.666 on dataset A and0.902 on dataset B, the ablation studies also prove the efficiency of eachfeature. Code is publicly available athttps://github.com/im0qianqian/WSDM2022TGP-AntGraph.

An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022