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 676–700 of 1570 papers

TitleStatusHype
FedGTA: Topology-aware Averaging for Federated Graph LearningCode0
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training StrategyCode0
Haar-Laplacian for directed graphsCode0
Graph Retention Networks for Dynamic GraphsCode0
Graph Neural Networks with Local Graph ParametersCode0
GraphSeqLM: A Unified Graph Language Framework for Omic Graph LearningCode0
Graph Neural Networks for Brain Graph Learning: A SurveyCode0
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph LearningCode0
Open-World Lifelong Graph LearningCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
Graph Learning from Filtered Signals: Graph System and Diffusion Kernel IdentificationCode0
Federated Graph Semantic and Structural LearningCode0
AGALE: A Graph-Aware Continual Learning Evaluation FrameworkCode0
Robust Graph Representation Learning for Local Corruption RecoveryCode0
HeGMN: Heterogeneous Graph Matching Network for Learning Graph SimilarityCode0
INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural NetworksCode0
Federated Graph Learning for Cross-Domain Recommendation—0
Federated Graph Learning -- A Position Paper—0
Continual Learning for Smart City: A Survey—0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
Federated Graph Condensation with Information Bottleneck Principles—0
Continual Graph Learning: A Survey—0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning—0
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning—0
Show:102550
← PrevPage 28 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified