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 1401–1425 of 1570 papers

TitleStatusHype
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning—0
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks—0
FedGIG: Graph Inversion from Gradient in Federated Learning—0
FedGL: Federated Graph Learning Framework with Global Self-Supervision—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Nonlinear Causal Discovery for Grouped Data—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs—0
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction—0
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning—0
FedRGL: Robust Federated Graph Learning for Label Noise—0
SLRL: Structured Latent Representation Learning for Multi-view Clustering—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Efficient and Stable Graph Scattering Transforms via Pruning—0
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction—0
FiGLearn: Filter and Graph Learning using Optimal Transport—0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction—0
Efficient and Robust Continual Graph Learning for Graph Classification in Biology—0
Effective and Efficient Graph Learning for Multi-view Clustering—0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified