Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
Nurudin Alvarez-Gonzalez Andreas Kaltenbrunner Vicenç Gómez
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.