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 701–725 of 1570 papers

TitleStatusHype
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future DirectionsCode1
How Expressive are Graph Neural Networks in Recommendation?Code1
Network Momentum across Asset Classes—0
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification—0
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning—0
Investigating the Interplay between Features and Structures in Graph LearningCode0
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
Graph Relation Aware Continual Learning—0
Language is All a Graph NeedsCode2
Learning on Graphs with Out-of-Distribution NodesCode1
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals—0
Differentially Private Graph Neural Network with Importance-Grained Noise Adaption—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
XGBD: Explanation-Guided Graph Backdoor DetectionCode0
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-SmoothingCode0
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction—0
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis—0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
Unsupervised Multiplex Graph Learning with Complementary and Consistent InformationCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach—0
TimeGNN: Temporal Dynamic Graph Learning for Time Series ForecastingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified