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 1026–1050 of 1570 papers

TitleStatusHype
Joint Graph and Vertex Importance Learning—0
Graph Learning from Gaussian and Stationary Graph Signals—0
Siamese Graph Learning for Semi-supervised Age EstimationCode0
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Heterogeneous Graph Learning for Acoustic Event ClassificationCode0
Image Coding via Perceptually Inspired Graph Learning—0
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery—0
Steering Graph Neural Networks with Pinning Control—0
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices—0
Fair Attribute Completion on Graph with Missing AttributesCode0
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning—0
Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering—0
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs—0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Graph Construction using Principal Axis Trees for Simple Graph ConvolutionCode0
Equivariant Polynomials for Graph Neural Networks—0
Time-varying Signals Recovery via Graph Neural Networks—0
Do We Really Need Complicated Model Architectures For Temporal Networks?—0
On the Expressivity of Persistent Homology in Graph Learning—0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning—0
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT—0
G-Signatures: Global Graph Propagation With Randomized Signatures—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
Distances for Markov Chains, and Their DifferentiationCode0
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified