Paper List

• Graph reduction with spectral and cut guarantees - coarsening/tree/v1.1) |
• **A Survey on Graph Condensation**
• Graph Condensation: A Survey
• A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
• Graph reduction with spectral and cut guarantees - coarsening/tree/v1.1) |
• Graph condensation for graph neural networks
• Attend who is weak: Enhancing graph condensation via cross-free adversarial training - |
• A unifying framework for spectrum-preserving graph sparsification and coarsening - Unifying-Framework-for-Spectrum-Preserving-Graph-Sparsification-and-Coarsening) |
• Scaling up graph neural networks via graph coarsening - Up-Graph-Neural-Networks-Via-Graph-Coarsening) |
• GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding - zhang/GraphZoom) |
• Graph coarsening with preserved spectral properties - umd/spectral-coarsening) |
• Featured graph coarsening with similarity guarantees - |
• Cat: Balanced continual graph learning with graph condensation - CGL) |
• Unsupervised learning of graph hierarchical abstractions with differentiable coarsening and optimal transport
• Graph coarsening via convolution matching for scalable graph neural network training - science/convolution-matching) |
• Graph Condensation via Receptive Field Distribution Matching - |
• Kernel Ridge Regression-Based Graph Dataset Distillation
• FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks - |
• Fast graph condensation with structure-based neural tangent kernel - SNTK | aiXiv 2023 | - |
• Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data - Zheng/SFGC) |
• Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation - |
• Multiple sparse graphs condensation - Based Systems 2023 | - |
• Graph condensation for inductive node representation learning - |
• Does graph distillation see like vision dataset counterpart?
• Graph condensation via eigenbasis matching - |
• Condensing Graphs via One-Step Gradient Matching