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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 1570 papers

TitleStatusHype
Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and InvestigationCode1
roadscene2vec: A Tool for Extracting and Embedding Road Scene-GraphsCode1
Fragmented Layer Grouping in GUI Designs Through Graph Learning Based on Multimodal InformationCode1
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario UnderstandingCode1
An Efficient Subgraph GNN with Provable Substructure Counting PowerCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph LearningCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph LearningCode1
Contrastive Graph Learning for Population-based fMRI ClassificationCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Embedding Words in Non-Vector Space with Unsupervised Graph LearningCode1
DyGKT: Dynamic Graph Learning for Knowledge TracingCode1
HSG-12M: A Large-Scale Spatial Multigraph DatasetCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
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
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Efficient Heterogeneous Graph Learning via Random ProjectionCode1
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly DetectionCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Environment-Aware Dynamic Graph Learning for Out-of-Distribution GeneralizationCode1
Learning from Counterfactual Links for Link PredictionCode1
Evaluating and Improving Graph-based Explanation Methods for Multi-Agent CoordinationCode1
Covariant Compositional Networks For Learning GraphsCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Dynamically Expandable Graph Convolution for Streaming RecommendationCode1
Fast and Distributed Equivariant Graph Neural Networks by Virtual Node LearningCode1
Fast Optimizer BenchmarkCode1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
FedHGN: A Federated Framework for Heterogeneous Graph Neural NetworksCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Data Augmentation for Deep Graph Learning: A SurveyCode1
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed GraphsCode1
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-DesignCode1
Generative 3D Part Assembly via Dynamic Graph LearningCode1
Generative Causal Explanations for Graph Neural NetworksCode1
NCAGC: A Neighborhood Contrast Framework for Attributed Graph ClusteringCode1
Dynamic Attentive Graph Learning for Image RestorationCode1
Continuity Preserving Online CenterLine Graph LearningCode1
Gradient Gating for Deep Multi-Rate Learning on GraphsCode1
GRAND+: Scalable Graph Random Neural NetworksCode1
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph LearningCode1
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future DirectionsCode1
Show:102550
← PrevPage 6 of 32Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified