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 691–700 of 982 papers

TitleStatusHype
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
SpecTRA: Spectral Transformer for Graph Representation Learning—0
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning—0
BCDR: Betweenness Centrality-based Distance Resampling for Graph Shortest Distance Embedding—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Scalable Hierarchical Embeddings of Complex Networks—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Towards Feature Overcorrelation in Deeper Graph Neural Networks—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
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Benchmark Results

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