Research map / Graph learning / GNN architectures
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add distance encodings as node features | GCN · Brazil-Airports, USA-Airports, C.elegans link prediction, NS link prediction, PB link prediction, C.elegans triangle prediction, NS triangle prediction | accuracy or AUC | Helped |
| Add distance encodings as node features | GCN · Europe-Airports | Average Accuracy | Mixed |
| Use landing probabilities instead of SPD as node-feature encodings | DE-GNN · Brazil-Airports, Europe-Airports, USA-Airports | Average Accuracy | Helped |
| Use landing probabilities instead of SPD as node-feature encodings | DE-GNN · C.elegans, NS, and PB link prediction; C.elegans triangle prediction | AUC | Hurt |
| Add SPD encodings to control multi-hop message aggregation | DE-GNN-SPD · Most tasks | accuracy or AUC: by almost 1% across most of the tasks | Helped |
| Replace SEAL's ego-network graph readout with set-pooled DE-2 features | Link prediction · C.elegans, NS, PB | AUC | Helped |
| Use SPD distance encoding for link prediction rather than landing probabilities | DE-GNN · C.elegans, NS, PB | AUC | Helped |
Closest papers by meaning
- GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction (NeurIPS 2026 · similarity 0.91)
- Bridging Theory and Practice in Link Representation with Graph Neural Networks (NeurIPS 2025 · similarity 0.90)
- Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-Based Similarity (AAAI 2024 · similarity 0.90)
- N2GON: Neural Networks for Graph-of-Net with Position Awareness (ICML 2025 · similarity 0.90)
- Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks (NeurIPS 2024 · similarity 0.90)
- Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning (ICLR 2025 · similarity 0.90)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.