Research map / Graph learning / GNN architectures
SpeqNets: Sparsity-aware permutation-equivariant graph networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Restrict tuple coloring to tuples inducing at most one connected component | (2,1)-LWL · Enzymes | accuracy: 50.6 ± 1.2 -> 53.7 ± 1.7 | Helped |
| Restrict tuple coloring to tuples inducing at most one connected component | (2,1)-LWL · NCI1 | accuracy: 84.4 ± 0.3 -> 82.8 ± 0.3 | Hurt |
| Add the + variant's neighborhood multiplicity information (ablation) | (2,1)-LWL · Reddit-Binary | accuracy: 76.9 ± 0.6 -> 94.7 ± 0.3 | Helped |
| Add the + variant's neighborhood multiplicity information (ablation) | (2,1)-LWL · Mutag | accuracy: 84.2 ± 1.7 -> 79.6 ± 3.4 | Hurt |
| Increase the connected-component limit from one to two (ablation) | LWL with k=2 · Enzymes | accuracy: 53.7 ± 1.7 -> 55.9 ± 1.0 | Helped |
Closest papers by meaning
- Schur Nets: exploiting local structure for equivariance in higher order graph neural networks (NeurIPS 2024 · similarity 0.91)
- Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models (NeurIPS 2025 · similarity 0.89)
- REVISITING MULTI-PERMUTATION EQUIVARIANCE THROUGH THE LENS OF IRREDUCIBLE REPRESENTATIONS (ICLR 2025 · similarity 0.89)
- Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning (NeurIPS 2025 · similarity 0.89)
- A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening (NeurIPS 2024 · similarity 0.89)
- On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks (NeurIPS 2024 · similarity 0.88)
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