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 1101–1110 of 1192 papers

TitleStatusHype
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning—0
From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules—0
Knowledge-enriched Two-layered Attention Network for Sentiment Analysis—0
Improving Knowledge Graph Embedding Using Simple ConstraintsCode0
RECS: Robust Graph Embedding Using Connection Subgraphs—0
Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction—0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding—0
Hyperbolic Entailment Cones for Learning Hierarchical EmbeddingsCode1
Expeditious Generation of Knowledge Graph EmbeddingsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified