We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats. The proposed encoding is sufficient to distinguish Strongly Regular Graphs, a family of challenging 3-WL equivalent graphs. We show theoretically that such encoding is more expressive than node-based sub-graph MP-GNNs. In an empirical evaluation on four benchmarks with 10 graph datasets, our results match or improve previous baselines on expressivity, graph classification, graph regression, and proximity tasks -- while reducing memory usage by 18.1x in certain real-world settings.

翻译：我们提出了一种新颖的边级自我网络编码方法，用于图学习，能够通过提供额外的节点和边特征或扩展消息传递格式来增强消息传递图神经网络（MP-GNN）。所提出的编码足以区分强正则图——一类具有挑战性的3-WL等价图族。我们从理论上证明，该编码比基于节点的子图MP-GNN更具表达力。在包含10个图数据集的四个基准测试中的实证评估显示，我们的结果在表达性、图分类、图回归和邻近性任务上匹配或超越了先前基线，同时在特定实际场景中将内存使用量降低了18.1倍。