SOTAVerified

Graph Embedding

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

( Image credit: GAT )

Papers

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Showing 411–420 of 1192 papers

TitleStatusHype
Wasserstein Graph Distance Based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman SubtreesCode0
Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation—0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph CompletionCode0
Neural Networks in a Product of Hyperbolic Spaces—0
Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution—0
Assessing the Effects of Hyperparameters on Knowledge Graph Embedding QualityCode0
A Representation Learning Framework for Property GraphsCode1
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
On the Surprising Behaviour of node2vecCode0
Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified