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 1276–1300 of 1570 papers

TitleStatusHype
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms—0
Graph Learning based Recommender Systems: A ReviewCode0
GIPA: General Information Propagation Algorithm for Graph LearningCode1
Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertainty—0
Robust Graph Learning Under Wasserstein Uncertainty—0
Neural Graph Matching based Collaborative FilteringCode1
FedGL: Federated Graph Learning Framework with Global Self-Supervision—0
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
Graph Learning: A Survey—0
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification—0
Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data—0
AdaGNN: Graph Neural Networks with Adaptive Frequency Response FilterCode1
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training StrategyCode0
SGL: Spectral Graph Learning from Measurements—0
Generative Causal Explanations for Graph Neural NetworksCode1
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
AutoGL: A Library for Automated Graph LearningCode1
Mutual Graph Learning for Camouflaged Object DetectionCode1
Graph Intention Network for Click-through Rate Prediction in Sponsored Search—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Expanding Semantic Knowledge for Zero-shot Graph Embedding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified