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 801–825 of 1570 papers

TitleStatusHype
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
Data-centric Graph Learning: A Survey—0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
Human Learning of Hierarchical Graphs—0
A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements—0
Data-centric Federated Graph Learning with Large Language Models—0
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs—0
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling—0
Hybrid Model-based / Data-driven Graph Transform for Image Coding—0
HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment—0
HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning—0
Data-Augmented Counterfactual Learning for Bundle Recommendation—0
Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data—0
Hyperbolic Geometry in Computer Vision: A Survey—0
Curvature-based Clustering on Graphs—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Subgraph Clustering and Atom Learning for Improved Image Classification—0
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer—0
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning—0
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction—0
Identifying critical nodes in complex networks by graph representation learning—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified