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 826–850 of 1570 papers

TitleStatusHype
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
Cross-Graph Learning of Multi-Relational Associations—0
SVGraph: Learning Semantic Graphs from Instructional Videos—0
Improved large-scale graph learning through ridge spectral sparsification—0
Improving Collaborative Filtering Recommendation via Graph Learning—0
Improving Facial Attribute Recognition by Group and Graph Learning—0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Synthetic Graph Generation to Benchmark Graph Learning—0
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis—0
Incremental Learning on Growing Graphs—0
Indirect Gaussian Graph Learning beyond Gaussianity—0
Cost-Optimal Learning of Causal Graphs—0
Inductive and Unsupervised Representation Learning on Graph Structured Objects—0
Inductive Collaborative Filtering via Relation Graph Learning—0
Inductive detection of Influence Operations via Graph Learning—0
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models—0
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance—0
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning—0
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation—0
A Generative Graph Method to Solve the Travelling Salesman Problem—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified