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 1151–1175 of 1570 papers

TitleStatusHype
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps—0
DURENDAL: Graph deep learning framework for temporal heterogeneous networks—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation—0
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection—0
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
Dynamic Graph Condensation—0
Dynamic Graph Learning based on Graph Laplacian—0
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks—0
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection—0
Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Algebraic graph learning of protein-ligand binding affinity—0
Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
Dynamic Sequential Graph Learning for Click-Through Rate Prediction—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks—0
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression—0
Edge-boosted graph learning for functional brain connectivity analysis—0
Edge-Featured Graph Attention Network—0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified