LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN
Graph Neural Networks (GNNs) suffer from two fundamental limitations: over-smoothing, where node representations become indistinguishable with depth, and over-squashing, where long-range information is compressed through limited message-passing channels. Existing metrics such as Dirichlet energy provide global characterizations of over-smoothing but lack the resolution to analyze node-level behavior and guide architectural improvements. In this paper, we propose LEED (Local Embedding Evolution Distance), a novel local metric that quantifies over-smoothing by tracking the evolution of individual node embeddings across layers. By operating at the node level, LEED enables fine-grained analys...
| Authors | Killian Cressant, Pedro B. Velloso |
|---|---|
| Primary category | cs.LG |
| Published | 10 August 2026 |
| Last updated at source | 4 September 2026 |
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