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

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Showing 421–430 of 982 papers

TitleStatusHype
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
Is Distance Matrix Enough for Geometric Deep Learning?Code1
A Survey on Spectral Graph Neural Networks—0
Heterophily-Aware Graph Attention Network—0
Spectral Augmentations for Graph Contrastive Learning—0
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal KrigingCode1
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
LazyGNN: Large-Scale Graph Neural Networks via Lazy PropagationCode1
Simultaneous Linear Multi-view Attributed Graph Representation Learning and ClusteringCode1
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
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Benchmark Results

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