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 1401–1425 of 1570 papers

TitleStatusHype
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
Graph Convolutional Networks for Graphs Containing Missing FeaturesCode1
Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information—0
Scaling Graph Neural Networks with Approximate PageRankCode1
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
Non-Parametric Graph Learning for Bayesian Graph Neural Networks—0
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph LearningCode0
Graph Learning for Inverse Landscape Genetics—0
Progressive Graph Learning for Open-Set Domain AdaptationCode1
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node EmbeddingsCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Wasserstein Embedding for Graph LearningCode1
NodeNet: A Graph Regularised Neural Network for Node Classification—0
Self-supervised Learning: Generative or Contrastive—0
Generative 3D Part Assembly via Dynamic Graph LearningCode1
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
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified