Research map / Graph learning / Knowledge and temporal graphs
Adaptive Mixture of Disentangled Experts for Dynamic Graphs under Distribution Shifts
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
- Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification (NeurIPS 2026 · similarity 0.88)
- GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts (NeurIPS 2024 · similarity 0.87)
- A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation (AAAI 2025 · similarity 0.87)
- Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation (ICLR 2025 · similarity 0.87)
- Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph (ICML 2025 · similarity 0.86)
- Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization (NeurIPS 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.
