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 1476–1500 of 1570 papers

TitleStatusHype
GRASPEL: Graph Spectral Learning at Scale—0
Spectral Graph Transformer Networks for Brain Surface Parcellation—0
Exponential Family Graph Embeddings—0
GLMNet: Graph Learning-Matching Networks for Feature Matching—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Heterogeneous Graph Learning for Visual Commonsense ReasoningCode0
Neighborhood and Graph Constructions using Non-Negative Kernel RegressionCode0
Decoupling feature propagation from the design of graph auto-encoders—0
Relational Graph Learning for Crowd NavigationCode0
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised ClassificationCode0
Attributed Graph Learning with 2-D Graph Convolution—0
DEEP GRAPH SPECTRAL EVOLUTION NETWORKS FOR GRAPH TOPOLOGICAL TRANSFORMATION—0
Iterative Deep Graph Learning for Graph Neural Networks—0
Structured Graph Learning Via Laplacian Spectral ConstraintsCode0
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer LearningCode0
Towards Federated Graph Learning for Collaborative Financial Crimes Detection—0
Multi-graph Fusion for Multi-view Spectral ClusteringCode0
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural NetworksCode0
Latent Multi-view Semi-Supervised ClassificationCode0
GmCN: Graph Mask Convolutional Network—0
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural NetworksCode0
Feature Interaction-aware Graph Neural Networks—0
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding ModelsCode0
Feature Graph Learning for 3D Point Cloud Denoising—0
Show:102550
← PrevPage 60 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified