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 501–525 of 1570 papers

TitleStatusHype
Dynamic Sequential Graph Learning for Click-Through Rate Prediction—0
Feature Graph Learning for 3D Point Cloud Denoising—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning—0
Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation—0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
Federated Graph Learning -- A Position Paper—0
Federated Graph Learning for Cross-Domain Recommendation—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification—0
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection—0
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer—0
GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks—0
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks—0
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions—0
Dynamic Graph Learning based on Graph Laplacian—0
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation—0
Supercharging Graph Transformers with Advective Diffusion—0
Dynamic Graph Condensation—0
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification—0
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis—0
A novel hybrid time-varying graph neural network for traffic flow forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified