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.
@article{arxiv.2312.05905,
title = {Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings},
author = {Nurudin Alvarez-Gonzalez and Andreas Kaltenbrunner and Vicenç Gómez},
journal= {arXiv preprint arXiv:2312.05905},
year = {2024}
}