graph coarsening

HC2-GNN: Hierarchical Graph Representation Learning for Efficient Text Classification AAAI 2026 Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement UAI 2026 Sample Weighted Incomplete Multimodal Clustering Based on Graph Coarsening Label Extraction AAAI 2026 Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systems AAAI 2025 From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks ICLR 2025 GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing IJCAI 2025 Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training AAAI 2025 A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation IJCAI 2024 A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening NeurIPS 2024 A Topology-aware Graph Coarsening Framework for Continual Graph Learning NeurIPS 2024 Graph Coarsening with Message-Passing Guarantees NeurIPS 2024 Optimization Framework for Semi-supervised Attributed Graph Coarsening UAI 2024 Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning ICML 2024 UGC: Universal Graph Coarsening NeurIPS 2024 A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening ICML 2023 A Unified Framework for Optimization-Based Graph Coarsening JMLR 2023 Featured Graph Coarsening with Similarity Guarantees ICML 2023 Generalizing Downsampling from Regular Data to Graphs AAAI 2023 Hierarchical Graph Transformer with Adaptive Node Sampling NeurIPS 2022 Graph Coarsening with Neural Networks ICLR 2021 Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning NeurIPS 2021 Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport AAAI 2021 Faster Graph Embeddings via Coarsening ICML 2020 Graph Coarsening with Preserved Spectral Properties AISTATS 2020 GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding ICLR 2020