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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 201–250 of 1570 papers

TitleStatusHype
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
Graph Matching with Bi-level Noisy CorrespondenceCode1
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning BenchmarksCode1
Heterogeneous Graph Learning for Multi-modal Medical Data AnalysisCode1
Spatio-Temporal Meta-Graph Learning for Traffic ForecastingCode1
Federated Learning on Non-IID Graphs via Structural Knowledge SharingCode1
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
pyGSL: A Graph Structure Learning ToolkitCode1
Time-aware Random Walk Diffusion to Improve Dynamic Graph LearningCode1
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research ChallengesCode1
A Practical, Progressively-Expressive GNNCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planningCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Gradient Gating for Deep Multi-Rate Learning on GraphsCode1
Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph LearningCode1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashingCode1
Motif-based Graph Representation Learning with Application to Chemical MoleculesCode1
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking GraphCode1
SCARA: Scalable Graph Neural Networks with Feature-Oriented OptimizationCode1
Learning Long-Term Spatial-Temporal Graphs for Active Speaker DetectionCode1
Graph-based Molecular Representation LearningCode1
TREE-G: Decision Trees Contesting Graph Neural NetworksCode1
Pure Transformers are Powerful Graph LearnersCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Long Range Graph BenchmarkCode1
NCAGC: A Neighborhood Contrast Framework for Attributed Graph ClusteringCode1
All the World's a (Hyper)Graph: A Data DramaCode1
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large GraphsCode1
GRETEL: A unified framework for Graph Counterfactual Explanation EvaluationCode1
Approximate Network Motif Mining Via Graph LearningCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Automatic Relation-aware Graph Network ProliferationCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge TransferCode1
Sparse Graph Learning from Spatiotemporal Time SeriesCode1
GraphHD: Efficient graph classification using hyperdimensional computingCode1
Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order InteractionsCode1
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph LearningCode1
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
Graph neural networks and attention-based CNN-LSTM for protein classificationCode1
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image SegmentationCode1
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected GraphsCode1
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural NetworksCode1
Contrastive Graph Learning for Population-based fMRI ClassificationCode1
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding AffinityCode1
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal TransportCode1
PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified