Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data
Graph neural networks (GNNs) excel at predictive tasks on graph-structured data but often lack the ability to incorporate symbolic domain knowledge and perform general reasoning. Relational Bayesian Networks (RBNs), in c…
Node ClassificationExploring Adaptive Structure Learning for Heterophilic Graphs
Graph Convolutional Networks (GCNs) gained traction for graph representation learning, with recent attention on improving performance on heterophilic graphs for various real-world applications. The localized feature aggr…
Graph Representation LearningNode ClassificationWho Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks
Graph-structured data is prevalent in many real-world applications, including social networks, financial systems, and molecular biology. Graph Neural Networks (GNNs) have become the de facto standard for learning from su…
Node ClassificationFederated LearningGraph LearningWhen Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
Class-imbalanced graph node classification is a practical yet underexplored research problem. Although recent studies have attempted to address this issue, they typically assume clean and reliable labels when processing …
Node ClassificationLeveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-world heterophilic graphs. Existing model…
Node ClassificationGraph LearningGraph Attention Specialized Expert Fusion Model for Node Classification: Based on Cora and Pubmed Datasets
Graph node classification is a fundamental task in graph neural networks (GNNs), aiming to assign predefined class labels to nodes. On the PubMed citation network dataset, we observe significant classification difficulty…
Graph ClassificationNode ClassificationQuantizing Text-attributed Graphs for Semantic-Structural Integration
Text-attributed graphs (TAGs) have emerged as a powerful representation for modeling complex relationships across diverse domains. With the rise of large language models (LLMs), there is growing interest in leveraging th…
Node ClassificationTransfer LearningGraph LearningResearch on the application of graph data structure and graph neural network in node classification/clustering tasks
Graph-structured data are pervasive across domains including social networks, biological networks, and knowledge graphs. Due to their non-Euclidean nature, such data pose significant challenges to conventional machine le…
Graph Representation LearningGraph Neural NetworkNode ClassificationKnowledge GraphsReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions
In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode specific inductive biases (e.g., acting as low-pass or high-pass filters), …
Node ClassificationGraph Neural Networks Powered by Encoder Embedding for Improved Node Learning
Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or minimally informed initial feature represen…
Graph Neural NetworkNode ClassificationGraph LearningApplying Text Embedding Models for Efficient Analysis in Labeled Property Graphs
Labeled property graphs often contain rich textual attributes that can enhance analytical tasks when properly leveraged. This work explores the use of pretrained text embedding models to enable efficient semantic analysi…
Node ClassificationHeterogeneous Graph Prompt Learning via Adaptive Weight Pruning
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based tasks (e.g., node classification or link prediction). Despite their triumphs, GNNs still face challenges such as long training and infe…
Node ClassificationLink PredictionTransferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits
Graph representation learning on Analog-Mixed Signal (AMS) circuits is crucial for various downstream tasks, e.g., parasitic estimation. However, the scarcity of design data, the unbalanced distribution of labels, and th…
Graph Representation LearningContrastive LearningNode ClassificationUniOD: A Universal Model for Outlier Detection across Diverse Domains
Outlier detection (OD), distinguishing inliers and outliers in completely unlabeled datasets, plays a vital role in science and engineering. Although there have been many insightful OD methods, most of them require troub…
Node ClassificationOutlier DetectionNTSFormer: A Self-Teaching Graph Transformer for Multimodal Isolated Cold-Start Node Classification
Isolated cold-start node classification on multimodal graphs is challenging because such nodes have no edges and often have missing modalities (e.g., absent text or image features). Existing methods address structural is…
Node ClassificationGraph LearningBit-Flip Fault Attack: Crushing Graph Neural Networks via Gradual Bit Search
Graph Neural Networks (GNNs) have emerged as a powerful machine learning method for graph-structured data. A plethora of hardware accelerators has been introduced to meet the performance demands of GNNs in real-world app…
Node ClassificationSimplifying Graph Kernels for Efficient
While kernel methods and Graph Neural Networks offer complementary strengths, integrating the two has posed challenges in efficiency and scalability. The Graph Neural Tangent Kernel provides a theoretical bridge by inter…
Node ClassificationDemystifying Distributed Training of Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) are powerful tools for solving graph-related problems. Distributed GNN frameworks and systems enhance the scalability of GNNs and accelerate model training, yet most are optimized for node cl…
graph partitioningLink PredictionNode ClassificationPredictionEquivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph machine learning: how to build graph fou…
AllNode ClassificationNode Property PredictionProperty PredictionDelving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification tasks but struggle with label noise in real-world data. Existing studies on graph learning with label noise commonly rely on …
BenchmarkingGraph LearningNode Classification