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 1081–1090 of 1192 papers

TitleStatusHype
Learning Combinatorial Optimization Algorithms over GraphsCode0
Learning graph representations of biochemical networks and its application to enzymatic link predictionCode0
From Node Embedding To Community Embedding : A Hyperbolic ApproachCode0
Learning grounded word meaning representations on similarity graphsCode0
Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D RotationsCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge GraphCode0
Learning Representations using Spectral-Biased Random Walks on GraphsCode0
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph CompletionCode0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph CompletionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified