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 1251–1275 of 1570 papers

TitleStatusHype
Relational Graph Learning for Grounded Video Description Generation—0
Graphical Models in Heavy-Tailed Markets—0
Accurately Solving Rod Dynamics with Graph Learning—0
Structure-Aware Random Fourier Kernel for Graphs—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
Sparse Graph Learning Under Laplacian-Related Constraints—0
Spectral Transform Forms Scalable TransformerCode0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters—0
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Graph Structural Attack by Perturbing Spectral DistanceCode0
Deconvolutional Networks on Graph Data—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Towards a Taxonomy of Graph Learning Datasets—0
PROMPT: Parallel Iterative Algorithm for _p norm linear regression via Majorization Minimization with an application to semi-supervised graph learning—0
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles—0
Tackling the Local Bias in Federated Graph Learning—0
Learning Time-Varying Graphs from Online Data—0
Accelerated Graph Learning from Smooth Signals—0
Time-varying Graph Learning Under Structured Temporal Priors—0
Online Graph Learning in Dynamic Environments—0
Stable Prediction on Graphs with Agnostic Distribution Shift—0
Graph Representation Learning for Spatial Image Steganalysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified