Research map / Graph learning / GNN architectures
Dynamic Graph Message Passing Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace fully connected message passing with dynamically sampled nodes and node-conditioned filters and affinities | DGMN · Cityscapes and COCO scene-understanding tasks | task performance | Helped |
| Use dynamic filters and affinities | DGMN · Non-local comparison | task performance | No clear effect |
| Use dynamic filters and affinities | DGMN · Non-local comparison | FLOPs: 9.4% | Helped |
| Use dynamic filters and affinities | DGMN · Non-local comparison | parameters: 25.3% | Helped |
Closest papers by meaning
- Towards Dynamic Message Passing on Graphs (NeurIPS 2024 · similarity 0.91)
- Improving the Effective Receptive Field of Message-Passing Neural Networks (ICML 2025 · similarity 0.87)
- Accurate and Scalable Graph Neural Networks via Message Invariance (ICLR 2025 · similarity 0.87)
- Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching (ICML 2025 · similarity 0.86)
- Recurrent Distance Filtering for Graph Representation Learning (ICML 2024 · similarity 0.86)
- Probabilistic Graph Rewiring via Virtual Nodes (NeurIPS 2024 · similarity 0.86)
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