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 941–950 of 982 papers

TitleStatusHype
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Towards Interpretable Molecular Graph Representation Learning—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Adaptive Graph Representation Learning for Video Person Re-identificationCode0
Graph Representation Learning: A SurveyCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
ChainNet: Learning on Blockchain Graphs with Topological Features—0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
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Benchmark Results

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