论文标题
一种有效的基于图的时间链接预测的方法：WSDM杯2022的第一名
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
论文作者
论文摘要
作为时间图中最关键的工作之一，时间链接预测吸引了研究领域的广泛关注。 WSDM CUP 2022寻求的解决方案可以预测时间跨度在时间图上的边缘的存在概率。本文介绍了Antgraph的解决方案，该解决方案赢得了比赛中的第一名。我们首先通过删除时间信息来分析性能的理论上界限，这意味着仅在图上结构和属性信息才能实现出色的性能。基于此假设，我们介绍了几个精心设计的特征。最后，在竞争数据集上进行的实验显示了我们的提案的优越性，该提案在数据集A上的AUC得分为0.666，在数据集B上达到0.902，消融研究也证明了每个功能的效率。代码可在https://github.com/im0qianqian/wsdm2022tgp-antgraph上公开获取。
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 seeks for solutions that predict the existence probabilities of edges within time spans over temporal graph. This paper introduces the solution of AntGraph, which wins the 1st place in the competition. We first analysis the theoretical upper-bound of the performance by removing temporal information, which implies that only structure and attribute information on the graph could achieve great performance. Based on this hypothesis, then we introduce several well-designed features. Finally, experiments conducted on the competition datasets show the superiority of our proposal, which achieved AUC score of 0.666 on dataset A and 0.902 on dataset B, the ablation studies also prove the efficiency of each feature. Code is publicly available at https://github.com/im0qianqian/WSDM2022TGP-AntGraph.
