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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1350 of 1570 papers

TitleStatusHype
A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Online Graph Topology Learning from Matrix-valued Time Series—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
A Linkage-based Doubly Imbalanced Graph Learning Framework for Face ClusteringCode0
PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels—0
Hippocampal Spatial Mapping As Fast Graph Learning—0
Multi-modal Graph Learning for Disease Prediction—0
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning—0
Multiple Graph Learning for Scalable Multi-view Clustering—0
Fund2Vec: Mutual Funds Similarity using Graph Learning—0
MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning—0
Exploring the Representational Power of Graph AutoencoderCode0
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation—0
Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection—0
Regularization of Mixture Models for Robust Principal Graph LearningCode0
G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification—0
Graph Domain Adaptation: A Generative View—0
Noise-robust Graph Learning by Estimating and Leveraging Pairwise InteractionsCode0
Graph Neural Networks with Local Graph ParametersCode0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
Keyframe-Focused Visual Imitation Learning—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Psycholinguistic Tripartite Graph Network for Personality Detection—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Time-Series Graph Network for Sea Surface Temperature Prediction—0
Graph2Graph Learning with Conditional Autoregressive Models—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Heterogeneous Graph Neural Network via Attribute Completion—0
A Survey on Optimal Transport for Machine Learning: Theory and Applications—0
Learning Clause Representation from Dependency-Anchor Graph for Connective PredictionCode0
_2-norm Flow Diffusion in Near-Linear Time—0
Improving Facial Attribute Recognition by Group and Graph Learning—0
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation—0
Local, global and scale-dependent node rolesCode0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
Federated Graph Learning -- A Position Paper—0
Consensus Graph Learning for Multi-view ClusteringCode0
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms—0
Graph Learning based Recommender Systems: A ReviewCode0
Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertainty—0
Robust Graph Learning Under Wasserstein Uncertainty—0
FedGL: Federated Graph Learning Framework with Global Self-Supervision—0
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
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training StrategyCode0
SGL: Spectral Graph Learning from Measurements—0
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified