Research map / Graph learning / Graph algorithms and clustering
Computationally-efficient Graph Modeling with Refined Graph Random Features
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
- General Graph Random Features (ICLR 2024 · similarity 0.93)
- Manifold Random Features (NeurIPS 2026 · similarity 0.88)
- Learning Long Range Dependencies on Graphs via Random Walks (ICLR 2025 · similarity 0.86)
- Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs (ICML 2025 · similarity 0.86)
- Revisiting Random Walks for Learning on Graphs (ICLR 2025 · similarity 0.85)
- From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers (ICML 2025 · 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.
