Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification
Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoo…
Node ClassificationRepresentation LearningOptimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
We delve into the challenge of semi-supervised node classification on the Contextual Stochastic Block Model (CSBM) dataset. Here, nodes from the two-cluster Stochastic Block Model (SBM) are coupled with feature vectors, …
Node ClassificationregressionStochastic Block ModelTransductive LearningLeveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs
Graph neural networks (GNNs) have become the preferred models for node classification in graph data due to their robust capabilities in integrating graph structures and attributes. However, these models heavily depend on…
ClassificationNode ClassificationEdge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model
Graph contrastive learning (GCL) aims to learn representations from unlabeled graph data in a self-supervised manner and has developed rapidly in recent years. However, edgelevel contrasts are not well explored by most e…
Contrastive LearningLink PredictionNode ClassificationTINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation
Graph Neural Networks (GNNs) are pivotal in graph-based learning, particularly excelling in node classification. However, their scalability is hindered by the need for multi-hop data during inference, limiting their appl…
Node ClassificationAGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio $\lambda$ in the image domain. Recently, the concept of mixup has been adapted to the gr…
Data AugmentationNode ClassificationGrimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks
Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial poisoning attacks on node classification tasks. Current defensive methods require substituting the original GNNs with defense …
Adversarial RobustnessClassificationNode ClassificationEdge-Splitting MLP: Node Classification on Homophilic and Heterophilic Graphs without Message Passing
Message Passing Neural Networks (MPNNs) have demonstrated remarkable success in node classification on homophilic graphs. It has been shown that they do not solely rely on homophily but on neighborhood distributions of n…
Node ClassificationMixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through self-attention, yet face scalability and …
Computational EfficiencyGraph Representation LearningMixture-of-Experts+3Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent…
Contrastive LearningGraph Representation LearningGraph structure learningKnowledge Graphs+2A novel robust integrating method by high-order proximity for self-supervised attribute network embedding
Attribute network embedding faces significant challenges, primarily integrating heterogeneous information and managing outliers. In this paper, we introduce a novel Robust Integrating Method by High-order Proximity for S…
AttributeData VisualizationGraph Outlier DetectionLink Prediction+2Hyperedge Anomaly Detection with Hypergraph Neural Network
Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associ…
Anomaly DetectionLink PredictionNode ClassificationTraining MLPs on Graphs without Supervision
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive app…
Fraud DetectionGraph ClassificationGraph LearningInductive Bias+3Graph Neural Networks Need Cluster-Normalize-Activate Modules
Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data. Despite their successful and diverse applications, oversmoothing prohibits deep architectures due to node features converging…
Node ClassificationProperty PredictionNode Classification With Integrated Reject Option
One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option setting is not previously explored. In this paper, we …
ClassificationGraph LearningNode ClassificationSA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such as node classification and link predictio…
Link PredictionNeural Architecture SearchNode ClassificationFrom ChebNet to ChebGibbsNet
Recent advancements in Spectral Graph Convolutional Networks (SpecGCNs) have led to state-of-the-art performance in various graph representation learning tasks. To exploit the potential of SpecGCNs, we analyze correspond…
Graph Representation LearningNode ClassificationRepresentation LearningOne Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require careful domain-specific architecture designs …
Link PredictionNode ClassificationExact Certification of (Graph) Neural Networks Against Label Poisoning
Machine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustness certificates is important to guarant…
Bilevel OptimizationNode ClassificationMixture of Experts for Node Classification
Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based…
ClassificationMixture-of-ExpertsNode Classification