Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Random Wavelet Features for Graph Kernel Machines
Node embeddings map graph vertices into low-dimensional Euclidean spaces while preserving structural information. They are central to tasks such as node classification, link prediction, and signal reconstruction. A key g…
Graph Representation LearningNode ClassificationLink PredictionInformation Geometry of Absorbing Markov-Chain and Discriminative Random Walks
Discriminative Random Walks (DRWs) are a simple yet powerful tool for semi-supervised node classification, but their theoretical foundations remain fragmentary. We revisit DRWs through the lens of information geometry, t…
Node ClassificationHomophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in tasks such as node classification, link prediction, and graph representation learning. However, they remain susceptible to biases that can…
Graph Representation LearningGraph Neural NetworkNode ClassificationLink PredictionBayesian Neighborhood Adaptation for Graph Neural Networks
The neighborhood scope (i.e., number of hops) where graph neural networks (GNNs) aggregate information to characterize a node's statistical property is critical to GNNs' performance. Two-stage approaches, training and va…
Node ClassificationATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
Counterfactual explanations offer an intuitive way to interpret graph neural networks (GNNs) by identifying minimal changes that alter a model's prediction, thereby answering "what must differ for a different outcome?". …
Explanation GenerationNode ClassificationAdversarial AttackGraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
Learning universal graph representations across heterogeneous domains is difficult because graph datasets differ in topology, node-attribute semantics, feature dimensions, and even attribute availability. We propose Grap…
Representation LearningCross-Domain Few-ShotGraph ClassificationNode ClassificationEnhancing Imbalanced Node Classification via Curriculum-Guided Feature Learning and Three-Stage Attention Network
Imbalanced node classification in graph neural networks (GNNs) happens when some labels are much more common than others, which causes the model to learn unfairly and perform badly on the less common classes. To solve th…
Node ClassificationHyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignment
Many complex networks exhibit hierarchical, tree-like structures, making hyperbolic space a natural candidate wherein to learn representations of them. Based on this observation, Hyperbolic Graph Neural Networks (HGNNs) …
Representation LearningNode ClassificationLink PredictionPIMPC-GNN: Physics-Informed Multi-Phase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks
Graph neural networks (GNNs) often struggle in class-imbalanced settings, where minority classes are under-represented and predictions are biased toward majorities. We propose \textbf{PIMPC-GNN}, a physics-informed multi…
Node ClassificationGraph LearningTemporal Graph Pattern Machine
Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving patterns that govern network transformation. However, prevailing methods ar…
Node ClassificationLink PredictionGraph LearningOptiMAG: Structure-Semantic Alignment via Unbalanced Optimal Transport
Multimodal Attributed Graphs (MAGs) have been widely adopted for modeling complex systems by integrating multi-modal information, such as text and images, on nodes. However, we identify a discrepancy between the implicit…
Representation LearningNode ClassificationLink PredictionScalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characteristics. Recent research shows that topology-preserving coarsening metho…
Node ClassificationA Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models
Combinatorial and topological structures, such as graphs, simplicial complexes, and cell complexes, form the foundation of geometric and topological deep learning (GDL and TDL) architectures. These models aggregate signa…
Node ClassificationCommunity DetectionBeyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification
Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, existing Parameter Finetuning (PaFT) para…
Node ClassificationGraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations. These methods can be broadly categorized…
Node ClassificationGraph LearningFSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation
Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to produce reliable estimates, and may propa…
Node ClassificationLink PredictionImpact of Graph Structure on Membership-Inference Risk for Graph Neural Networks
Graph neural networks (GNNs) are widely used for tasks such as node classification and link prediction, but their use in sensitive settings raises concerns about training-data leakage. Prior work on privacy leakage in GN…
Node ClassificationLink PredictionSemi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs
The emergent reasoning capabilities of Large Language Models (LLMs) offer a transformative paradigm for analyzing text-attributed graphs. While instruction tuning is the prevailing method for adapting pre-trained LLMs to…
Node ClassificationGraph LearningSimple Network Graph Comparative Learning
The effectiveness of contrastive learning methods has been widely recognized in the field of graph learning, especially in contexts where graph data often lack labels or are difficult to label. However, the application o…
Contrastive LearningNode ClassificationGraph LearningPLGC: Pseudo-Labeled Graph Condensation
Large graph datasets make training graph neural networks (GNNs) computationally costly. Graph condensation methods address this by generating small synthetic graphs that approximate the original data. However, existing a…
Node ClassificationLink Prediction