SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

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Showing 1501–1550 of 1570 papers

TitleStatusHype
Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure PurificationCode0
Robust Graph Learning from Noisy DataCode0
Robust Graph Neural Network based on Graph DenoisingCode0
ROD: Reception-aware Online Distillation for Sparse GraphsCode0
S2FGL: Spatial Spectral Federated Graph LearningCode0
Scalable and Flexible Causal Discovery with an Efficient Test for AdjacencyCode0
Scalable Graph Generative Modeling via Substructure SequencesCode0
Scalable Graph Learning for Anti-Money Laundering: A First LookCode0
Scalable Hypergraph Structure Learning with Diverse Smoothness PriorsCode0
Scalable Structure Learning for Sparse Context-Specific SystemsCode0
Scale Invariance of Graph Neural NetworksCode0
ScaleNet: Scale Invariance Learning in Directed GraphsCode0
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node EmbeddingCode0
Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based AugmentationsCode0
Semi-Supervised Graph Classification: A Hierarchical Graph PerspectiveCode0
Semi-Supervised Graph Learning Meets Dimensionality ReductionCode0
Semi-Supervised Learning With Graph Learning-Convolutional NetworksCode0
SGAT: Simplicial Graph Attention NetworkCode0
Siamese Graph Learning for Semi-supervised Age EstimationCode0
Simple Path Structural Encoding for Graph TransformersCode0
Simplifying Node Classification on Heterophilous Graphs with Compatible Label PropagationCode0
SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image ClassificationCode0
SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction IdentificationCode0
Sparse Graph Attention NetworksCode0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
Spectral Transform Forms Scalable TransformerCode0
SPGL: Enhancing Session-based Recommendation with Single Positive Graph LearningCode0
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional NetworksCode0
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
Video action detection by learning graph-based spatio-temporal interactionsCode0
Homophily modulates double descent generalization in graph convolution networksCode0
Stratified Graph SpectraCode0
Structured Graph Learning Via Laplacian Spectral ConstraintsCode0
Structure-Preference Enabled Graph Embedding Generation under Differential PrivacyCode0
Graph-Based Representation Learning of Neuronal Dynamics and BehaviorCode0
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate InferenceCode0
Temporal Link Prediction Using Graph Embedding DynamicsCode0
Temporal Multiresolution Graph Neural Networks For Epidemic PredictionCode0
Temporal receptive field in dynamic graph learning: A comprehensive analysisCode0
Tensor-based Graph Learning with Consistency and Specificity for Multi-view ClusteringCode0
Tensor-view Topological Graph Neural NetworkCode0
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding ModelsCode0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Topological Pooling on GraphsCode0
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of ThingsCode0
TouchUp-G: Improving Feature Representation through Graph-Centric FinetuningCode0
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsCode0
Towards Faster Graph Partitioning via Pre-training and Inductive InferenceCode0
Towards Real-Time Temporal Graph LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified