Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
NC-NCD: Novel Class Discovery for Node Classification
Novel Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. However, existing NCD methods often struggle to maintain a bal…
ClassificationKnowledge DistillationNode ClassificationNovel Class DiscoveryPre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs
The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on …
Few-Shot LearningGraph Neural NetworkLanguage ModelingLanguage Modelling+3Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing
We delve into the issue of node classification within graphs, specifically reevaluating the concept of neighborhood aggregation, which is a fundamental component in graph neural networks (GNNs). Our analysis reveals conc…
ClassificationNode ClassificationMeta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning wit…
Contrastive LearningGraph LearningMeta-LearningNode ClassificationEnhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
Graphs are pervasive in the real-world, such as social network analysis, bioinformatics, and knowledge graphs. Graph neural networks (GNNs) have great ability in node classification, a fundamental task on graphs. Unfortu…
Active LearningKnowledge GraphsMeta-LearningNode Classification+2GraphFM: A Scalable Framework for Multi-Graph Pretraining
Graph neural networks are typically trained on individual datasets, often requiring highly specialized models and extensive hyperparameter tuning. This dataset-specific approach arises because each graph dataset often ha…
Node ClassificationRelaxing Graph Transformers for Adversarial Attacks
Existing studies have shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Even though Graph Transformers (GTs) surpassed Message-Passing GNNs on several benchmarks, their adversarial robustness…
Adversarial RobustnessGraph ClassificationNode ClassificationHyperAggregation: Aggregating over Graph Edges with Hypernetworks
HyperAggregation is a hypernetwork-based aggregation function for Graph Neural Networks. It uses a hypernetwork to dynamically generate weights in the size of the current neighborhood, which are then used to aggregate th…
Graph ClassificationGraph RegressionNode ClassificationGraph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting …
Graph ClassificationNode ClassificationPrompt LearningWhen Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node and edge types exist in the graph, and he…
BenchmarkingGraph LearningGraph MiningModel Selection+1GeoMix: Towards Geometry-Aware Data Augmentation
Mixup has shown considerable success in mitigating the challenges posed by limited labeled data in image classification. By synthesizing samples through the interpolation of features and labels, Mixup effectively address…
Data AugmentationGraph Learningimage-classificationImage Classification+2Improving Hyperbolic Representations via Gromov-Wasserstein Regularization
Hyperbolic representations have shown remarkable efficacy in modeling inherent hierarchies and complexities within data structures. Hyperbolic neural networks have been commonly applied for learning such representations …
Few-Shot Image Classificationimage-classificationImage ClassificationLink Prediction+1Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustne…
Computational EfficiencyGraph LearningNode ClassificationSlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification
Existing WSI analysis methods lie on the consensus that histopathological characteristics of tumors are significant guidance for cancer diagnostics. Particularly, as the evolution of cancers is a continuous process, the …
graph constructionGraph LearningGraph Neural Networkimage-classification+4Conformal Inductive Graph Neural Networks
Conformal prediction (CP) transforms any model's output into prediction sets guaranteed to include (cover) the true label. CP requires exchangeability, a relaxation of the i.i.d. assumption, to obtain a valid distributio…
Conformal PredictionNode ClassificationPredictionvalidUnsupervised Graph Representation Learning with Inductive Shallow Node Embedding
Network science has witnessed a surge in popularity, driven by the transformative power of node representation learning for diverse applications like social network analysis and biological modeling. While shallow embeddi…
Graph Representation LearningInductive LearningNode ClassificationRepresentation LearningSTAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs
We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our appro…
Graph Neural NetworkNode ClassificationSF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network
Graph Neural Network (GNN), with the main idea of encoding graph structure information of graphs by propagation and aggregation, has developed rapidly. It achieved excellent performance in representation learning of mult…
Graph Neural NetworkKnowledge GraphsLink PredictionNode Classification+1Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
Graph node classification with few labeled nodes presents significant challenges due to limited supervision. Conventional methods often exploit the graph in a transductive learning manner. They fail to effectively utiliz…
Graph Neural NetworkMultiview LearningNode ClassificationTransductive LearningA Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs
Geodesic distances on manifolds have numerous applications in image processing, computer graphics and computer vision. In this work, we introduce an approach called `LGGD' (Learned Generalized Geodesic Distances). This m…
Node Classification