{"total":68,"rows":[{"id":"2204.07054","title":"BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks","created":"2022-03-17","cited_by_count":75,"area":null},{"id":"2206.11168","title":"Ordered Subgraph Aggregation Networks","created":"2022-06-22","cited_by_count":57,"area":null},{"id":"2204.07321","title":"Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities","created":"2022-04-15","cited_by_count":52,"area":null},{"id":"2305.08573","title":"A graph convolutional autoencoder approach to model order reduction for parametrized PDEs","created":"2023-05-15","cited_by_count":44,"area":null},{"id":"2304.01575","title":"The expressive power of pooling in Graph Neural Networks","created":"2023-04-04","cited_by_count":35,"area":null},{"id":"2310.15978","title":"Graph Deep Learning for Time Series Forecasting","created":"2023-10-24","cited_by_count":26,"area":null},{"id":"2503.03313","title":"LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models","created":"2025-03-05","cited_by_count":22,"area":null},{"id":"2305.19183","title":"Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting","created":"2023-05-30","cited_by_count":17,"area":null},{"id":"2402.10634","title":"Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling","created":"2024-02-16","cited_by_count":17,"area":null},{"id":"2306.08854","title":"A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening","created":"2023-06-15","cited_by_count":15,"area":null},{"id":"2405.13526","title":"Understanding Virtual Nodes: Oversquashing and Node Heterogeneity","created":"2024-05-22","cited_by_count":14,"area":null},{"id":"2308.06960","title":"Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks","created":"2023-08-14","cited_by_count":13,"area":null},{"id":"2310.02600","title":"Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks","created":"2023-10-04","cited_by_count":13,"area":null},{"id":"2501.16944","title":"Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks","created":"2025-01-28","cited_by_count":13,"area":null},{"id":"2407.04236","title":"Graph Pooling via Ricci Flow","created":"2024-07-05","cited_by_count":10,"area":null},{"id":"2409.08036","title":"Heterogeneous Sheaf Neural Networks","created":"2024-09-12","cited_by_count":10,"area":null},{"id":"2402.02000","title":"A Survey on Graph Condensation","created":"2024-02-03","cited_by_count":9,"area":null},{"id":"2402.16346","title":"Boosting Graph Pooling with Persistent Homology","created":"2024-02-26","cited_by_count":9,"area":null},{"id":"2405.18127","title":"Graph Coarsening with Message-Passing Guarantees","created":"2024-05-28","cited_by_count":9,"area":null},{"id":"2207.08779","title":"Simplifying Clustering with Graph Neural Networks","created":"2022-07-18","cited_by_count":8,"area":null},{"id":"2211.14425","title":"PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations","created":"2022-11-26","cited_by_count":8,"area":null},{"id":"2410.06746","title":"Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention","created":"2024-10-09","cited_by_count":8,"area":null},{"id":"2206.07015","title":"SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction","created":"2022-05-25","cited_by_count":7,"area":null},{"id":"2211.06218","title":"Total Variation Graph Neural Networks","created":"2022-11-11","cited_by_count":7,"area":null},{"id":"2406.09031","title":"A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability","created":"2024-06-13","cited_by_count":7,"area":null},{"id":"2210.05490","title":"Pooling Strategies for Simplicial Convolutional Networks","created":"2022-10-11","cited_by_count":5,"area":null},{"id":"2306.09386","title":"Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting","created":"2023-06-15","cited_by_count":5,"area":null},{"id":"2409.05100","title":"MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks","created":"2024-09-08","cited_by_count":5,"area":null},{"id":"2506.11700","title":"Geometry-Aware Edge Pooling for Graph Neural Networks","created":"2025-06-13","cited_by_count":5,"area":null},{"id":"2301.01741","title":"Graph State-Space Models and Latent Relational Inference","created":"2023-01-04","cited_by_count":4,"area":null},{"id":"2304.05099","title":"Feudal Graph Reinforcement Learning","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2306.12726","title":"On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling","created":"2023-06-22","cited_by_count":4,"area":null},{"id":"2310.07068","title":"Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose","created":"2023-10-10","cited_by_count":3,"area":null},{"id":"2407.03760","title":"GraphCNNpred: A stock market indices prediction using a Graph based deep learning system","created":"2024-07-04","cited_by_count":3,"area":null},{"id":"2411.03223","title":"Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation","created":"2024-11-05","cited_by_count":3,"area":null},{"id":"2204.13429","title":"DOTIN: Dropping Task-Irrelevant Nodes for GNNs","created":"2022-04-28","cited_by_count":2,"area":null},{"id":"2207.06027","title":"Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search","created":"2022-07-13","cited_by_count":2,"area":null},{"id":"2210.00854","title":"Deep learning and multi-level featurization of graph representations of microstructural data","created":"2022-09-29","cited_by_count":2,"area":null},{"id":"2210.03123","title":"On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2410.18103","title":"A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection","created":"2024-10-08","cited_by_count":2,"area":null},{"id":"2501.09821","title":"BN-Pool: Bayesian Nonparametric Pooling for Graphs","created":"2025-01-16","cited_by_count":2,"area":null},{"id":"2507.23604","title":"Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2512.12642","title":"Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks","created":"2025-12-14","cited_by_count":2,"area":null},{"id":"2308.09907","title":"Imputing Brain Measurements Across Data Sets via Graph Neural Networks","created":"2023-08-19","cited_by_count":1,"area":null},{"id":"2311.12644","title":"Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes","created":"2023-11-21","cited_by_count":1,"area":null},{"id":"2402.09234","title":"Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks","created":"2024-02-14","cited_by_count":1,"area":null},{"id":"2408.06486","title":"Implicit Neural Representation For Accurate CFD Flow Field Prediction","created":"2024-08-12","cited_by_count":1,"area":null},{"id":"2409.10263","title":"MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length","created":"2024-09-16","cited_by_count":1,"area":null},{"id":"2410.06969","title":"DLGNet: Hyperedge Classification through Directed Line Graphs for Chemical Reactions","created":"2024-10-09","cited_by_count":1,"area":null},{"id":"2605.06250","title":"The Role of Node Features in Graph Pooling","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2609.14962","title":"Geometric Flow enhanced Graph Coarsening","created":"2026-09-14","cited_by_count":1,"area":null},{"id":"2305.06315","title":"NervePool: A Simplicial Pooling Layer","created":"2023-05-10","cited_by_count":0,"area":null},{"id":"2311.00860","title":"Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning","created":"2023-11-01","cited_by_count":0,"area":null},{"id":"2401.10300","title":"A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems","created":"2024-01-18","cited_by_count":0,"area":null},{"id":"2402.09603","title":"Scalable Graph Self-Supervised Learning","created":"2024-02-14","cited_by_count":0,"area":null},{"id":"2503.05560","title":"Global graph features unveiled by unsupervised geometric deep learning","created":"2025-03-07","cited_by_count":0,"area":null},{"id":"2510.00374","title":"GDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2512.05623","title":"Bounded Graph Clustering with Graph Neural Networks","created":"2025-12-05","cited_by_count":0,"area":null},{"id":"2512.06752","title":"Multi-Scale Protein Structure Modelling with Geometric Graph U-Nets","created":"2025-12-07","cited_by_count":0,"area":null},{"id":"2512.14855","title":"A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materials","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2512.18232","title":"AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation","created":"2025-12-20","cited_by_count":0,"area":null},{"id":"2605.06466","title":"Diversity Curves for Graph Representation Learning","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.18387","title":"Graph Hierarchical Recurrence for Long-Range Generalization","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.20932","title":"Hierarchical Pooling for Sheaf Neural Networks","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2607.12026","title":"Learning the Graphical Nature of Symmetries","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.27767","title":"Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.03696","title":"Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2609.17061","title":"Repurposing Unified Topological Signatures for Graph Representation Learning","created":"2026-09-15","cited_by_count":0,"area":null}],"next_cursor":null}