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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–975 of 1570 papers

TitleStatusHype
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks—0
SLRL: Structured Latent Representation Learning for Multi-view Clustering—0
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction—0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction—0
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection—0
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging—0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
SoK: Differential Privacy on Graph-Structured Data—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning—0
Nonlinear Causal Discovery for Grouped Data—0
Sparse Graph Learning Under Laplacian-Related Constraints—0
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks—0
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation—0
Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems—0
Cross-Graph Learning of Multi-Relational Associations—0
Spatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking—0
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction—0
Spectral GNN via Two-dimensional (2-D) Graph Convolution—0
Spectral Graph Transformer Networks for Brain Surface Parcellation—0
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering—0
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling—0
SPGP: Structure Prototype Guided Graph Pooling—0
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified