Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Hyperbolic Heterogeneous Graph Transformer
In heterogeneous graphs, we can observe complex structures such as tree-like or hierarchical structures. Recently, the hyperbolic space has been widely adopted in many studies to effectively learn these complex structure…
Node ClassificationGraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learning
Recent advances in search-augmented large reasoning models (LRMs) enable the retrieval of external knowledge to reduce hallucinations in multistep reasoning. However, their ability to operate on graph-structured data, pr…
Node ClassificationLink PredictionGraph LearningParallelizing Node-Level Explainability in Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated remarkable performance in a wide range of tasks, such as node classification, link prediction, and graph classification, by exploiting the structural information in graph-st…
Graph ClassificationNode Classificationgraph partitioningLink PredictionRethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sensor networks. Existing studies mostly address relatively benign scenarios, name…
Node ClassificationPrompt Tuning without Labeled Samples for Zero-Shot Node Classification in Text-Attributed Graphs
Node classification is a fundamental problem in information retrieval with many real-world applications, such as community detection in social networks, grouping articles published online and product categorization in e-…
Product CategorizationInformation RetrievalNode ClassificationCommunity DetectionExplainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
Timely leak detection in water distribution networks is critical for conserving resources and maintaining operational efficiency. Although Graph Neural Networks (GNNs) excel at capturing spatial-temporal dependencies in …
Graph Neural NetworkNode ClassificationSpiking Heterogeneous Graph Attention Networks
Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing he…
Node ClassificationGraph LearningEnergy-Balanced Hyperspherical Graph Representation Learning via Structural Binding and Entropic Dispersion
Graph Representation Learning (GRL) can be fundamentally modeled as a physical process of seeking an energy equilibrium state for a node system on a latent manifold. However, existing Graph Neural Networks (GNNs) often s…
Graph Representation LearningNode ClassificationLink PredictionInterpretable and Adaptive Node Classification on Heterophilic Graphs via Combinatorial Scoring and Hybrid Learning
Graph neural networks (GNNs) achieve strong performance on homophilic graphs but often struggle under heterophily, where adjacent nodes frequently belong to different classes. We propose an interpretable and adaptive fra…
Computational EfficiencyNode ClassificationCoarse-to-Fine Open-Set Graph Node Classification with Large Language Models
Developing open-set classification methods capable of classifying in-distribution (ID) data while detecting out-of-distribution (OOD) samples is essential for deploying graph neural networks (GNNs) in open-world scenario…
Node ClassificationMedical DiagnosisFraud DetectionParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the over-smoothing problem in deep GNNs, global…
Graph Representation LearningGraph ClassificationNode ClassificationGraph LearningUnderstanding When Graph Convolutional Networks Help: A Diagnostic Study on Label Scarcity and Structural Properties
Graph Convolutional Networks (GCNs) have become a standard approach for semi-supervised node classification, yet practitioners lack clear guidance on when GCNs provide meaningful improvements over simpler baselines. We p…
Node ClassificationCORE: Contrastive Masked Feature Reconstruction on Graphs
In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked feature reconstruction (MFR), a generativ…
Self-Supervised LearningGraph ClassificationContrastive LearningNode ClassificationView Space: Learning Representation across Arbitrary Graphs
Generalizing pretrained models to unseen datasets without retraining is a central challenge toward foundation models. Achieving fully inductive inference on numerical data is particularly difficult due to large variation…
Representation LearningNode ClassificationTHeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation
Heterogeneous Graph Neural Networks (HGNNs) are effective for modeling Heterogeneous Information Networks (HINs), which encode complex multi-typed entities and relations. However, HGNNs often suffer from type information…
Node ClassificationCan TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
Foundation models pretrained on large data have demonstrated remarkable zero-shot generalization capabilities across domains. Building on the success of TabPFN for tabular data and its recent extension to time series, we…
Zero-shot GeneralizationFeature EngineeringNode ClassificationLocal-Curvature-Aware Knowledge Graph Embedding: An Extended Ricci Flow Approach
Knowledge graph embedding (KGE) relies on the geometry of the embedding space to encode semantic and structural relations. Existing methods place all entities on one homogeneous manifold, Euclidean, spherical, hyperbolic…
Knowledge Graph EmbeddingNode ClassificationLink PredictionEmpowering GNNs for Domain Adaptation via Denoising Target Graph
We explore the node classification task in the context of graph domain adaptation, which uses both source and target graph structures along with source labels to enhance the generalization capabilities of Graph Neural Ne…
GRAPH DOMAIN ADAPTATIONNode ClassificationCredal Graph Neural Networks
Uncertainty quantification is essential for deploying reliable Graph Neural Networks (GNNs), where existing approaches primarily rely on Bayesian inference or ensembles. In this paper, we introduce the first credal graph…
Node ClassificationBayesian InferenceModel-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Information
Graph Neural Networks (GNNs) have demonstrated exceptional efficacy in relational learning tasks, including node classification and link prediction. However, their application raises significant fairness concerns, as GNN…
Node ClassificationLink Prediction