Research map / Graph learning / Graph self-supervised learning
Discriminative Mixture-of-Experts on Graphs with Reliable Expert Fusion
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
- Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling (NeurIPS 2023 · similarity 0.93)
- Tight Clusters Make Specialized Experts (ICLR 2025 · similarity 0.89)
- A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts (ICML 2024 · similarity 0.87)
- Mixture of Weak and Strong Experts on Graphs (ICLR 2024 · similarity 0.87)
- Layerwise Recurrent Router for Mixture-of-Experts (ICLR 2025 · similarity 0.87)
- Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts (NeurIPS 2026 · similarity 0.87)
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.
