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 926–950 of 1570 papers

TitleStatusHype
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure—0
DeepGAR: Deep Graph Learning for Analogical ReasoningCode0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
Heterogeneous Graph Sparsification for Efficient Representation Learning—0
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
Hyperbolic Graph Representation Learning: A Tutorial—0
pyGSL: A Graph Structure Learning ToolkitCode1
Learning Product Graphs from Spectral Templates—0
Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling—0
Time-aware Random Walk Diffusion to Improve Dynamic Graph LearningCode1
Heterogeneous Trajectory Forecasting via Risk and Scene Graph LearningCode0
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs—0
Unrolled Graph Learning for Multi-Agent Collaboration—0
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome—0
Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs—0
Learning Graphical Factor Models with Riemannian OptimizationCode0
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research ChallengesCode1
Graph Few-shot Learning with Task-specific StructuresCode0
Data-Augmented Counterfactual Learning for Bundle Recommendation—0
A Practical, Progressively-Expressive GNNCode1
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis—0
Show:102550
← PrevPage 38 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified