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 1326–1350 of 1570 papers

TitleStatusHype
Time-Series Graph Network for Sea Surface Temperature Prediction—0
Graph2Graph Learning with Conditional Autoregressive Models—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Heterogeneous Graph Neural Network via Attribute Completion—0
A Survey on Optimal Transport for Machine Learning: Theory and Applications—0
Learning Clause Representation from Dependency-Anchor Graph for Connective PredictionCode0
_2-norm Flow Diffusion in Near-Linear Time—0
Improving Facial Attribute Recognition by Group and Graph Learning—0
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation—0
Local, global and scale-dependent node rolesCode0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
Federated Graph Learning -- A Position Paper—0
Consensus Graph Learning for Multi-view ClusteringCode0
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms—0
Graph Learning based Recommender Systems: A ReviewCode0
Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertainty—0
Robust Graph Learning Under Wasserstein Uncertainty—0
FedGL: Federated Graph Learning Framework with Global Self-Supervision—0
Graph Learning: A Survey—0
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification—0
Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data—0
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training StrategyCode0
SGL: Spectral Graph Learning from Measurements—0
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
Show:102550
← PrevPage 54 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified