Research map / Graph learning / Graph algorithms and clustering
The Multiscale Laplacian Graph Kernel
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 |
|---|---|---|---|
| Use MLG instead of the strongest reported baseline | Graph classification · MUTAG | accuracy: 85.50(± 2.50) -> 87.94(± 1.61) | Helped |
| Use MLG instead of the strongest reported baseline | Graph classification · PTC | accuracy: 60.18(± 2.19) -> 63.26(± 1.48) | Helped |
| Use MLG instead of the strongest reported baseline | Graph classification · ENZYMES | accuracy: 53.75(± 1.37) -> 61.81(± 0.99) | Helped |
| Use MLG instead of the strongest reported baseline | Graph classification · PROTEINS | accuracy: 75.61(± 0.45) -> 76.34(± 0.72) | Helped |
| Use MLG instead of the strongest reported baseline | Graph classification · NCI1 | accuracy: 84.76(± 0.32) -> 81.75(± 0.24) | Hurt |
| Use MLG instead of the strongest reported baseline | Graph classification · NCI109 | accuracy: 85.32(± 0.34) -> 81.31(± 0.22) | Hurt |
Closest papers by meaning
- Graph Filtration Kernels (AAAI 2022 · similarity 0.88)
- Isolation Graph Kernel (AAAI 2021 · similarity 0.87)
- Dynamic Kernel Graph Sparsifiers (ICLR 2026 · similarity 0.87)
- Structure-Aware Random Fourier Kernel for Graphs (NeurIPS 2021 · similarity 0.86)
- From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers (ICML 2025 · similarity 0.86)
- Generalizing Weisfeiler-Lehman Kernels to Subgraphs (ICLR 2025 · similarity 0.86)
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.