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 651–700 of 1570 papers

TitleStatusHype
Modeling Multi-Step Scientific Processes with Graph Transformer Networks—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy—0
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Knowledge Probing for Graph Representation Learning—0
Deep Generative Models for Subgraph PredictionCode0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph LearningCode0
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph LearningCode0
Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning—0
Lifelong Graph Learning for Graph SummarizationCode0
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction—0
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation—0
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency—0
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
Subgraph Clustering and Atom Learning for Improved Image Classification—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
GeoMix: Towards Geometry-Aware Data AugmentationCode0
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
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model—0
SLRL: Structured Latent Representation Learning for Multi-view Clustering—0
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation—0
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
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
Graph Edge Representation via Tensor Product Graph Convolutional Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified