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 726–750 of 1570 papers

TitleStatusHype
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