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 1226–1250 of 1570 papers

TitleStatusHype
ROD: Reception-aware Online Distillation for Sparse GraphsCode0
A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements—0
Online Graph Topology Learning from Matrix-valued Time Series—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—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
Bilinear Scoring Function Search for Knowledge Graph LearningCode1
Multi-modal Graph Learning for Disease Prediction—0
Hippocampal Spatial Mapping As Fast Graph Learning—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
GRAND: Graph Neural DiffusionCode1
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
Noise-robust Graph Learning by Estimating and Leveraging Pairwise InteractionsCode0
Graph Domain Adaptation: A Generative View—0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
Graph Neural Networks with Local Graph ParametersCode0
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified