node classification
A task in graph-based machine learning where the goal is to assign labels to nodes in a graph based on their features and their connection to other nodes.
- $\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
- Attack by Yourself: Effective and Unnoticeable Multi-Category Graph Backdoor Attacks with Subgraph Triggers Pool
- DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
- Enhancing Graph Classification Robustness with Singular Pooling
- GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
- GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
- Geometric Imbalance in Semi-Supervised Node Classification
- GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability
- GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
- How Particle System Theory Enhances Hypergraph Message Passing
- Interpretable and Parameter Efficient Graph Neural Additive Models with Random Fourier Features
- Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
- L2DGCN: Learnable Enhancement and Label Selection Dynamic Graph Convolutional Networks for Mitigating Degree Bias
- Memorization in Graph Neural Networks
- Pseudo-Riemannian Graph Transformer
- Restricted Global-Aware Graph Filters Bridging GNNs and Transformer for Node Classification
- Rethinking Tokenized Graph Transformers for Node Classification
- Robust Explanations of Graph Neural Networks via Graph Curvatures
- Siegel Neural Networks
- Spectral Graph Coarsening Using Inner Product Preservation and the Grassmann Manifold
- Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
- Taxonomy of reduction matrices for Graph Coarsening
- Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models