SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Showing 171180 of 982 papers

TitleStatusHype
A Gentle Introduction to Deep Learning for GraphsCode1
Evaluating Modules in Graph Contrastive LearningCode1
Jointly Learnable Data Augmentations for Self-Supervised GNNsCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Expander Graph PropagationCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node TasksCode1
Show:102550
← PrevPage 18 of 99Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47Unverified