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

TL;DR

This paper presents AntGraph, a graph learning approach for temporal link prediction that won first place in the WSDM Cup 2022, demonstrating high accuracy through well-designed features and theoretical analysis.

Contribution

Introduces AntGraph, a novel graph learning method for temporal link prediction, with theoretical performance analysis and effective feature design, achieving top results in a competitive benchmark.

Findings

Achieved AUC of 0.666 on dataset A

Achieved AUC of 0.902 on dataset B

Validated the effectiveness of each feature through ablation studies

Abstract

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.