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 1226–1250 of 1570 papers

TitleStatusHype
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
A novel hybrid time-varying graph neural network for traffic flow forecasting—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening—0
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning—0
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation—0
Feature Graph Learning for 3D Point Cloud Denoising—0
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning—0
FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning—0
Federated Graph Condensation with Information Bottleneck Principles—0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
Federated Graph Learning -- A Position Paper—0
Federated Graph Learning for Cross-Domain Recommendation—0
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks—0
Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
Federated Graph Learning with Graphless Clients—0
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer NetworksCode0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
Accuracy and stability of solar variable selection comparison under complicated dependence structuresCode0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph LearningCode0
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive ReviewCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified