arXiv (All)
open access
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 pro
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN#441812
- Reachability is not enough: Diagnosing long-range behavior in GNNs#1118747
- Beyond Fixed Features: Architecture-Dependent Sensitivity to Node Representations under Heterophily#1118837
- Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction#1128987
- Interpreting and Unifying Graph Neural Networks with An Optimization Framework#19725
Record · ID 971847
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive