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 821–830 of 1570 papers

TitleStatusHype
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction—0
Identifying critical nodes in complex networks by graph representation learning—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering—0
Image Coding via Perceptually Inspired Graph Learning—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction—0
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs—0
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified