graph neural networks
A class of neural networks designed to perform inference on data represented as graphs, capturing relationships and interactions between nodes.
- A Closer Look at Graph Transformers: Cross-Aggregation and Beyond
- A Signed Graph Approach to Understanding and Mitigating Oversmoothing
- AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
- Attack by Yourself: Effective and Unnoticeable Multi-Category Graph Backdoor Attacks with Subgraph Triggers Pool
- Axial Neural Networks for Dimension-Free Foundation Models
- Boundary-Value PDEs Meet Higher-Order Differential Topology-aware GNNs
- Bridging Theory and Practice in Link Representation with Graph Neural Networks
- Conditional Diffusion Anomaly Modeling on Graphs
- CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
- DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction
- Defining and Discovering Hyper-meta-paths for Heterogeneous Hypergraphs
- Diffusion-Guided Graph Data Augmentation
- Dynamic Bundling with Large Language Models for Zero-Shot Inference on Text-Attributed Graphs
- Effects of Dropout on Performance in Long-range Graph Learning Tasks
- Efficient Bayesian Experiment Design with Equivariant Networks
- Enhancing Graph Classification Robustness with Singular Pooling
- Explore In-Context Message Passing Operator for Graph Neural Networks in A Mean Field Game
- GAMMA: Gated Multi-hop Message Passing for Homophily-Agnostic Node Representation in GNNs
- GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
- GD$^2$: Robust Graph Learning under Label Noise via Dual-View Prediction Discrepancy
- GMV: A Unified and Efficient Graph Multi-View Learning Framework
- Generative Graph Pattern Machine
- Geometry-Aware Edge Pooling for Graph Neural Networks
- GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability
- GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability
- Graph Persistence goes Spectral
- GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
- GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
- HEIR: Learning Graph-Based Motion Hierarchies
- HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning
- Higher-Order Learning with Graph Neural Networks via Hypergraph Encodings
- Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
- Interpretable and Parameter Efficient Graph Neural Additive Models with Random Fourier Features
- L2DGCN: Learnable Enhancement and Label Selection Dynamic Graph Convolutional Networks for Mitigating Degree Bias
- Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
- Learning Repetition-Invariant Representations for Polymer Informatics
- Learning Sparse Approximate Inverse Preconditioners for Conjugate Gradient Solvers on GPUs
- Learning to Plan Like the Human Brain via Visuospatial Perception and Semantic-Episodic Synergistic Decision-Making
- Let Brain Rhythm Shape Machine Intelligence for Connecting Dots on Graphs
- LoSplit: Loss-Guided Dynamic Split for Training-Time Defense Against Graph Backdoor Attacks
- Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
- MOTION: Multi-Sculpt Evolutionary Coarsening for Federated Continual Graph Learning
- Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective
- Memorization in Graph Neural Networks
- Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting
- Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants
- Multi-order Orchestrated Curriculum Distillation for Model-Heterogeneous Federated Graph Learning
- Non-stationary Equivariant Graph Neural Networks for Physical Dynamics Simulation
- OASIS: One-Shot Federated Graph Learning via Wasserstein Assisted Knowledge Integration
- Object-Centric Representation Learning for Enhanced 3D Semantic Scene Graph Prediction
- On Local Limits of Sparse Random Graphs: Color Convergence and the Refined Configuration Model
- On Logic-based Self-Explainable Graph Neural Networks
- On Transferring Transferability: Towards a Theory for Size Generalization
- On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
- OpenGU: A Comprehensive Benchmark for Graph Unlearning
- Over-squashing in Spatiotemporal Graph Neural Networks
- Practical Bayes-Optimal Membership Inference Attacks
- Preference-driven Knowledge Distillation for Few-shot Node Classification
- Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
- Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
- Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks
- Restricted Global-Aware Graph Filters Bridging GNNs and Transformer for Node Classification
- Robust Explanations of Graph Neural Networks via Graph Curvatures
- SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs
- Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
- Self-Supervised Learning of Graph Representations for Network Intrusion Detection
- Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
- Sound Logical Explanations for Mean Aggregation Graph Neural Networks
- Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
- Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
- Taxonomy of reduction matrices for Graph Coarsening
- The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics
- The Underappreciated Power of Vision Models for Graph Structural Understanding
- Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
- Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
- Understanding and Enhancing Message Passing on Heterophilic Graphs via Compatibility Matrix
- UniGTE: Unified Graph–Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains
- What Expressivity Theory Misses: Message Passing Complexity for GNNs
- When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning
- You Only Spectralize Once: Taking a Spectral Detour to Accelerate Graph Neural Network