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 1426–1450 of 1570 papers

TitleStatusHype
Graph Random Neural Network for Semi-Supervised Learning on GraphsCode1
Understanding Negative Sampling in Graph Representation LearningCode1
Graph-based, Self-Supervised Program Repair from Diagnostic FeedbackCode1
Neural Stochastic Block Model & Scalable Community-Based Graph Learning—0
Semi-supervised Hypergraph Node Classification on Hypergraph Line ExpansionCode1
Inductive and Unsupervised Representation Learning on Graph Structured Objects—0
Graph Learning Approaches to Recommender Systems: A Review—0
Structured Landmark Detection via Topology-Adapting Deep Graph LearningCode1
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional NetworksCode1
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge TransferCode1
The general theory of permutation equivarant neural networks and higher order graph variational encodersCode1
Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation—0
Latent-Graph Learning for Disease Prediction—0
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
An Uncoupled Training Architecture for Large Graph Learning—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
Universal Function Approximation on GraphsCode1
Automating Botnet Detection with Graph Neural NetworksCode1
Unsupervised Graph Embedding via Adaptive Graph Learning—0
A Survey on The Expressive Power of Graph Neural Networks—0
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance—0
Uncovering Insurance Fraud Conspiracy with Network Learning—0
Show:102550
← PrevPage 58 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified