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 1551–1570 of 1570 papers

TitleStatusHype
Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties—0
Graph Learning based Generative Design for Resilience of Interdependent Network Systems—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges—0
Graph Learning-Convolutional Networks—0
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data—0
Learning Multi-layer Graphs and a Common Representation for Clustering—0
Graph Learning for Cognitive Digital Twins in Manufacturing Systems—0
Graph Learning for Inverse Landscape Genetics—0
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
Graph Learning for Planning: The Story Thus Far and Open Challenges—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling—0
Graph Learning from Gaussian and Stationary Graph Signals—0
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation—0
SPGP: Structure Prototype Guided Graph Pooling—0
Disentangled Generative Graph Representation Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified