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 1426–1450 of 1570 papers

TitleStatusHype
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricingCode0
Accuracy and stability of solar variable selection comparison under complicated dependence structuresCode0
Grale: Designing Networks for Graph Learning—0
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposingCode0
Are Hyperbolic Representations in Graphs Created Equal?—0
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?—0
A Generative Graph Method to Solve the Travelling Salesman Problem—0
Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph LearningCode0
Non-Parametric Graph Learning for Bayesian Graph Neural Networks—0
Graph Learning for Inverse Landscape Genetics—0
NodeNet: A Graph Regularised Neural Network for Node Classification—0
Self-supervised Learning: Generative or Contrastive—0
DeeperGCN: All You Need to Train Deeper GCNsCode0
Accurately Solving Physical Systems with Graph Learning—0
Graph Learning with Loss-Guided Training—0
Deep graph learning for semi-supervised classification—0
Neural Stochastic Block Model & Scalable Community-Based Graph Learning—0
Inductive and Unsupervised Representation Learning on Graph Structured Objects—0
Graph Learning Approaches to Recommender Systems: A Review—0
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation—0
Latent-Graph Learning for Disease Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified