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 676–700 of 1570 papers

TitleStatusHype
A Benchmark for Fairness-Aware Graph Learning—0
Joint Data Inpainting and Graph Learning via Unrolled Neural NetworksCode0
GeoMix: Towards Geometry-Aware Data AugmentationCode0
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation—0
Learning a Mini-batch Graph Transformer via Two-stage Interaction AugmentationCode0
Graph Transformers: A Survey—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image ClassificationCode0
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model—0
SLRL: Structured Latent Representation Learning for Multi-view Clustering—0
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation—0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsCode0
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNsCode0
Foundations and Frontiers of Graph Learning Theory—0
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide PropertiesCode0
Automated Knowledge Graph Learning in Industrial Processes—0
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration—0
Understanding Multistationarity of Fully Open Reaction Networks—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Federated Graph Semantic and Structural LearningCode0
Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search—0
Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning—0
Next Level Message-Passing with Hierarchical Support GraphsCode0
Graph Edge Representation via Tensor Product Graph Convolutional Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified