Research map / Graph learning / GNN architectures
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
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.
Closest papers by meaning
- Weisfeiler-Leman at the margin: When more expressivity matters (ICML 2024 · similarity 0.88)
- General Graph Random Features (ICLR 2024 · similarity 0.87)
- From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers (ICML 2025 · similarity 0.87)
- Revisiting Random Walks for Learning on Graphs (ICLR 2025 · similarity 0.86)
- Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning (NeurIPS 2024 · similarity 0.85)
- Non-convolutional graph neural networks. (NeurIPS 2024 · similarity 0.85)
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.