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 1071–1080 of 1192 papers

TitleStatusHype
Weight Prediction for Variants of Weighted Directed Networks—0
From communities to interpretable network and word embedding: an unified approach—0
From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules—0
From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma—0
Semi-supervised Graph Embedding Approach to Dynamic Link Prediction—0
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification—0
From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features—0
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction—0
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding—0
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified