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 901–925 of 1570 papers

TitleStatusHype
Graph Learning with Localized Neighborhood Fairness—0
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey—0
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Graph Convolutional Networks for Traffic Forecasting with Missing ValuesCode1
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Adversarial Weight Perturbation Improves Generalization in Graph Neural NetworksCode0
Multi-view Graph Convolutional Networks with Differentiable Node Selection—0
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation—0
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning BenchmarksCode1
Graph Matching with Bi-level Noisy CorrespondenceCode1
An open unified deep graph learning framework for discovering drug leadsCode0
Stars: Tera-Scale Graph Building for Clustering and Graph Learning—0
Joint graph learning from Gaussian observations in the presence of hidden nodesCode0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction—0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
Architectural Implications of Embedding Dimension during GCN on CPU and GPU—0
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces—0
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation—0
Heterogeneous Graph Learning for Multi-modal Medical Data AnalysisCode1
Spatio-Temporal Meta-Graph Learning for Traffic ForecastingCode1
Latent Graph Inference using Product Manifolds—0
PatchGT: Transformer over Non-trainable Clusters for Learning Graph RepresentationsCode0
Federated Learning on Non-IID Graphs via Structural Knowledge SharingCode1
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning ParadigmCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified