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 1176–1200 of 1570 papers

TitleStatusHype
Towards a Taxonomy of Graph Learning Datasets—0
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple MethodsCode1
Node Dependent Local Smoothing for Scalable Graph LearningCode1
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
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural NetworkCode1
Time-varying Graph Learning Under Structured Temporal Priors—0
Online Graph Learning in Dynamic Environments—0
Towards Open-World Feature Extrapolation: An Inductive Graph Learning ApproachCode1
Stable Prediction on Graphs with Agnostic Distribution Shift—0
3D Infomax improves GNNs for Molecular Property PredictionCode1
Graph Representation Learning for Spatial Image Steganalysis—0
Light Field Saliency Detection with Dual Local Graph Learning andReciprocative GuidanceCode1
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"Code1
On Locality in Graph Learning via Graph Neural Network—0
Weakly Supervised Graph Clustering—0
DEEP GRAPH TREE NETWORKS—0
Graph Information Matters: Understanding Graph Filters from Interaction Probability—0
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression—0
Interpreting Graph Neural Networks via Unrevealed Causal Learning—0
Understanding Graph Learning with Local Intrinsic Dimensionality—0
Robust Graph Data Learning with Latent Graph Convolutional Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified