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Graph Neural Network

Papers

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Showing 2001–2025 of 3598 papers

TitleStatusHype
Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks—0
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation—0
Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion—0
Uncertainty-Aware Robust Learning on Noisy Graphs—0
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach—0
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective—0
Understanding and Modeling Job Marketplace with Pretrained Language Models—0
Understanding GNNs for Boolean Satisfiability through Approximation Algorithms—0
Understanding Human Innate Immune System Dependencies using Graph Neural Networks—0
Understanding the Performance of Learning Precoding Policy with GNN and CNNs—0
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network—0
Unified Graph Structured Models for Video Understanding—0
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting—0
UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs—0
Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation—0
Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition—0
Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management—0
Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization—0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models—0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models—0
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information—0
Unsupervised Training for Neural TSP Solver—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Up-sampling-only and Adaptive Mesh-based GNN for Simulating Physical Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1air0S1—Unverified