Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dynamic node affinity prediction -- where the goal is to predict `how much' two nodes will interact in the future. In fact, simple heuristic approaches such as persistent forecasts and moving averages over \emph{ground-truth labels} significantly and consistently outperform TGNs. Building on this observation, we find that computing heuristics \textit{over messages} is an equally competitive approach, outperforming TGN and all current temporal graph (TG) models on dynamic node affinity prediction. In this paper, we prove that no formulation of TGN can represent persistent forecasting or moving averages over messages, and propose to enhance the expressivity of TGNs by adding source-target identification to each interaction event message. We show that this modification is required to represent persistent forecasting, moving averages, and the broader class of autoregressive models over messages. Our proposed method, TGNv2, significantly outperforms TGN and all current TG models on all Temporal Graph Benchmark (TGB) dynamic node affinity prediction datasets.

本文解决了时间图网络在动态节点亲和预测任务中的表现不佳问题。研究提出了一种通过增加源-目标识别的方式增强时间图网络的表达能力，表明这种修改对于表示持续预测和移动平均等方法是必要的。实验结果表明，所提出的方法TGNv2在所有时间图基准动态节点亲和预测数据集中显著超越了现有的时间图模型。

通过源-目标识别增强时间图网络的表达能力