Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering
The emergence of graph neural networks (GNNs) has offered a powerful tool for semi-supervised node classification tasks. Subsequent studies have achieved further improvements through refining the message passing schemes …
Node ClassificationData AugmentationGraph ClusteringSelf-Adaptive Graph Mixture of Models
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over graph-structured data, yet recent studies have shown that their performance gains are beginning to plateau. In many cases, well-established mo…
Graph ClassificationNode ClassificationLink PredictionGraph LearningInteractiveGNNExplainer: A Visual Analytics Framework for Multi-Faceted Understanding and Probing of Graph Neural Network Predictions
Graph Neural Networks (GNNs) excel in graph-based learning tasks, but their complex, non-linear operations often render them as opaque "black boxes". This opacity hinders user trust, complicates debugging, bias detection…
Graph Neural NetworkNode ClassificationBias DetectionImproving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spread
The rise of graph-structured data has driven major advances in Graph Machine Learning (GML), where graph embeddings (GEs) map features from Knowledge Graphs (KGs) into vector spaces, enabling tasks like node classificati…
Node ClassificationKnowledge GraphsLink PredictionA Systematic Study of Model Extraction Attacks on Graph Foundation Models
Graph machine learning has advanced rapidly in tasks such as link prediction, anomaly detection, and node classification. As models scale up, pretrained graph models have become valuable intellectual assets because they …
Node ClassificationAnomaly DetectionModel extractionFraud DetectionGraphSB: 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 LearningGraph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settings
This paper provides a comprehensive review of mainly GNN, DRL, and PTM methods with a focus on their potential incorporation in strategic multiagent settings. We draw interest in (i) ML methods currently utilized for unc…
Reinforcement LearningNode ClassificationLink PredictionDecision MakingSiegel Neural Networks
Riemannian symmetric spaces (RSS) such as hyperbolic spaces and symmetric positive definite (SPD) manifolds have become popular spaces for representation learning. In this paper, we propose a novel approach for building …
Representation LearningNode ClassificationLLM-Guided Dynamic-UMAP for Personalized Federated Graph Learning
We propose a method that uses large language models to assist graph machine learning under personalization and privacy constraints. The approach combines data augmentation for sparse graphs, prompt and instruction tuning…
Personalized Federated LearningKnowledge Graph CompletionNode ClassificationData AugmentationDP-AdamW: Investigating Decoupled Weight Decay and Bias Correction in Private Deep Learning
As deep learning methods increasingly utilize sensitive data on a widespread scale, differential privacy (DP) offers formal guarantees to protect against information leakage during model training. A significant challenge…
Image ClassificationNode ClassificationText ClassificationStuart-Landau Oscillatory Graph Neural Network
Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. In this work, we introduce the Complex-Va…
Graph ClassificationGraph Neural NetworkNode ClassificationGraph RegressionBeyond Fixed Depth: Adaptive Graph Neural Networks for Node Classification Under Varying Homophily
Graph Neural Networks (GNNs) have achieved significant success in addressing node classification tasks. However, the effectiveness of traditional GNNs degrades on heterophilic graphs, where connected nodes often belong t…
Node ClassificationGrassmanian Interpolation of Low-Pass Graph Filters: Theory and Applications
Low-pass graph filters are fundamental for signal processing on graphs and other non-Euclidean domains. However, the computation of such filters for parametric graph families can be prohibitively expensive as computation…
Node ClassificationTowards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias
Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs). However, an objective gap often remains between pre-training objectives and downstream tasks. To bridge …
Node ClassificationEnhancing Graph Classification Robustness with Singular Pooling
Graph Neural Networks (GNNs) have achieved strong performance across a range of graph representation learning tasks, yet their adversarial robustness in graph classification remains underexplored compared to node classif…
Graph Representation LearningAdversarial RobustnessGraph ClassificationNode ClassificationDoes Homophily Help in Robust Test-time Node Classification?
Homophily, the tendency of nodes from the same class to connect, is a fundamental property of real-world graphs, underpinning structural and semantic patterns in domains such as citation networks and social networks. Exi…
Graph structure learningNode ClassificationGraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data. As a common pattern to train powerful GNNs, the "pre-training, adaptation…
Node ClassificationGraph LearningAdaptive Graph Mixture of Residual Experts: Unsupervised Learning on Diverse Graphs with Heterogeneous Specialization
Graph Neural Networks (GNNs) face a fundamental adaptability challenge: their fixed message-passing architectures struggle with the immense diversity of real-world graphs, where optimal computational strategies vary by l…
Node ClassificationFew-Shot LearningParameter-Free Hypergraph Neural Network for Few-Shot Node Classification
Few-shot node classification on hypergraphs requires models that generalize from scarce labels while capturing high-order structures. Existing hypergraph neural networks (HNNs) effectively encode such structures but ofte…
Node ClassificationPruning and Quantization Impact on Graph Neural Networks
Graph neural networks (GNNs) are known to operate with high accuracy on learning from graph-structured data, but they suffer from high computational and resource costs. Neural network compression methods are used to redu…
Neural Network CompressionGraph ClassificationNode ClassificationLink Prediction