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 701–750 of 1570 papers

TitleStatusHype
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future DirectionsCode1
How Expressive are Graph Neural Networks in Recommendation?Code1
Network Momentum across Asset Classes—0
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification—0
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning—0
Investigating the Interplay between Features and Structures in Graph LearningCode0
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning—0
Graph Relation Aware Continual Learning—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
Language is All a Graph NeedsCode2
Learning on Graphs with Out-of-Distribution NodesCode1
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals—0
Differentially Private Graph Neural Network with Importance-Grained Noise Adaption—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
XGBD: Explanation-Guided Graph Backdoor DetectionCode0
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction—0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis—0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
Unsupervised Multiplex Graph Learning with Complementary and Consistent InformationCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach—0
TimeGNN: Temporal Dynamic Graph Learning for Time Series ForecastingCode1
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
An Empirical Evaluation of Temporal Graph BenchmarkCode1
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph LearningCode1
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
Graph Federated Learning Based on the Decentralized Framework—0
Curvature-based Clustering on Graphs—0
Examining the Effects of Degree Distribution and Homophily in Graph Learning ModelsCode1
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly DetectionCode1
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment—0
INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural NetworksCode0
RegExplainer: Generating Explanations for Graph Neural Networks in Regression TasksCode0
Neighbor group structure preserving based consensus graph learning for incomplete multi-view clustering—0
Influential Simplices Mining via Simplicial Convolutional Network—0
Generative Contrastive Graph Learning for RecommendationCode1
Towards Fair Graph Neural Networks via Graph CounterfactualCode1
Multimodal brain age estimation using interpretable adaptive population-graph learningCode1
Neural Causal Graph Collaborative FilteringCode0
Multi-modal Graph Learning over UMLS Knowledge GraphsCode1
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Learning from Heterogeneity: A Dynamic Learning Framework for HypergraphsCode0
Knowledge Graph Self-Supervised Rationalization for RecommendationCode1
STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic PredictionCode1
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion FunctionalsCode0
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution GeneralizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified