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 251–300 of 1570 papers

TitleStatusHype
Context-Aware Sparse Deep Coordination GraphsCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Heterogeneous Graph Learning for Multi-modal Medical Data AnalysisCode1
Graph Convolutional Networks for Traffic Forecasting with Missing ValuesCode1
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message PassingCode1
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
Graph Matching with Bi-level Noisy CorrespondenceCode1
Graph Learning for Numeric PlanningCode1
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph LearningCode1
Contrastive Graph Learning for Population-based fMRI ClassificationCode1
Deep Temporal Graph ClusteringCode1
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning BenchmarksCode1
Graph Neural Convection-Diffusion with HeterophilyCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNsCode1
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research ChallengesCode1
Convolutional Neural Networks on Graphs with Chebyshev Approximation, RevisitedCode1
HSG-12M: A Large-Scale Spatial Multigraph DatasetCode1
GraphLLM: Boosting Graph Reasoning Ability of Large Language ModelCode1
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly DetectionCode1
Graph neural networks and attention-based CNN-LSTM for protein classificationCode1
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node RepresentationCode1
AdaGNN: Graph Neural Networks with Adaptive Frequency Response FilterCode1
Learning from Counterfactual Links for Link PredictionCode1
DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement PredictionCode1
Covariant Compositional Networks For Learning GraphsCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
GraphHop: An Enhanced Label Propagation Method for Node ClassificationCode1
Disentangled Condensation for Large-scale GraphsCode1
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed GraphsCode1
Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order InteractionsCode1
Distance Recomputator and Topology Reconstructor for Graph Neural NetworksCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
TREE-G: Decision Trees Contesting Graph Neural NetworksCode1
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning ModelsCode1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
Data Augmentation for Deep Graph Learning: A SurveyCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
NCAGC: A Neighborhood Contrast Framework for Attributed Graph ClusteringCode1
Dynamically Expandable Graph Convolution for Streaming RecommendationCode1
DyGKT: Dynamic Graph Learning for Knowledge TracingCode1
Dynamic Attentive Graph Learning for Image RestorationCode1
SimMLP: Training MLPs on Graphs without SupervisionCode1
Continuity Preserving Online CenterLine Graph LearningCode1
High-Dimensional Bayesian Optimization via Tree-Structured Additive ModelsCode1
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future DirectionsCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph LearningCode1
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint PredictionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified