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 951–975 of 1570 papers

TitleStatusHype
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction—0
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis—0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
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
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Graph Federated Learning Based on the Decentralized Framework—0
Curvature-based Clustering on Graphs—0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
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
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Neural Causal Graph Collaborative FilteringCode0
Learning from Heterogeneity: A Dynamic Learning Framework for HypergraphsCode0
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion FunctionalsCode0
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions—0
Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving—0
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI—0
Directional diffusion models for graph representation learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified