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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 931–940 of 982 papers

TitleStatusHype
On the Interpretability and Evaluation of Graph Representation Learning—0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Learning Robust Representations with Graph Denoising Policy Network—0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Universal Graph Transformer Self-Attention NetworksCode0
Towards Interpretable Molecular Graph Representation Learning—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Adaptive Graph Representation Learning for Video Person Re-identificationCode0
Show:102550
← PrevPage 94 of 99Next →

Benchmark Results

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