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 711–720 of 1192 papers

TitleStatusHype
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction—0
Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning—0
Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding—0
Resilience in Knowledge Graph Embeddings—0
RESTORE: Graph Embedding Assessment Through Reconstruction—0
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation—0
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings—0
Riemannian TransE: Multi-relational Graph Embedding in Non-Euclidean Space—0
RiskSEA : A Scalable Graph Embedding for Detecting On-chain Fraudulent Activities on the Ethereum Blockchain—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified