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

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Showing 1051–1075 of 1570 papers

TitleStatusHype
Graph schemas as abstractions for transfer learning, inference, and planning—0
Efficient Graph Laplacian Estimation by Proximal NewtonCode0
Semi-decentralized Federated Ego Graph Learning for Recommendation—0
Outlier-Robust Gromov-Wasserstein for Graph DataCode0
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements—0
Quantum Graph Learning: Frontiers and Outlook—0
Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems—0
Graph Neural Operators for Classification of Spatial Transcriptomics Data—0
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs—0
Continual Graph Learning: A Survey—0
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective—0
Causal Bandits without Graph LearningCode0
Graph Learning Across Data Silos—0
Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity PredictionCode0
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction—0
AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural NetworkCode0
RELIANT: Fair Knowledge Distillation for Graph Neural NetworksCode0
Scene-Aware Label Graph Learning for Multi-Label Image Classification—0
Regularized Primitive Graph Learning for Unified Vector Mapping—0
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering—0
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View ClusteringCode0
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection—0
Homophily modulates double descent generalization in graph convolution networksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified