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 351–400 of 1570 papers

TitleStatusHype
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks—0
TorchGT: A Holistic System for Large-scale Graph Transformer Training—0
GLAudio Listens to the Sound of the GraphCode0
Temporal receptive field in dynamic graph learning: A comprehensive analysisCode0
A Benchmark for Fairness-Aware Graph Learning—0
Joint Data Inpainting and Graph Learning via Unrolled Neural NetworksCode0
Continuity Preserving Online CenterLine Graph LearningCode1
GeoMix: Towards Geometry-Aware Data AugmentationCode0
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph BenchmarkCode1
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation—0
Learning a Mini-batch Graph Transformer via Two-stage Interaction AugmentationCode0
Graph Transformers: A Survey—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image ClassificationCode0
SLRL: Structured Latent Representation Learning for Multi-view Clustering—0
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model—0
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation—0
MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based LearningCode2
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsCode0
Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNsCode0
Foundations and Frontiers of Graph Learning Theory—0
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide PropertiesCode0
Automated Knowledge Graph Learning in Industrial Processes—0
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration—0
Understanding Multistationarity of Fully Open Reaction Networks—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Federated Graph Semantic and Structural LearningCode0
Fast Optimizer BenchmarkCode1
KAGNNs: Kolmogorov-Arnold Networks meet Graph LearningCode2
GraphSnapShot: Caching Local Structure for Fast Graph LearningCode1
Distance Recomputator and Topology Reconstructor for Graph Neural NetworksCode1
Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search—0
Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning—0
Next Level Message-Passing with Hierarchical Support GraphsCode0
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity—0
Graph Edge Representation via Tensor Product Graph Convolutional Representation—0
MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders PredictionCode1
A Scalable and Effective Alternative to Graph Transformers—0
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed GraphsCode1
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language ModelsCode1
HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph LearningCode1
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning—0
A Comprehensive Survey of Foundation Models in Medicine—0
Towards Neural Scaling Laws for Foundation Models on Temporal GraphsCode1
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph LearningCode1
Dataset Condensation with Latent Quantile Matching—0
Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure—0
Scalable and Flexible Causal Discovery with an Efficient Test for AdjacencyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified