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 961–970 of 1192 papers

TitleStatusHype
A Survey on Graph Representation Learning Methods—0
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings—0
A Survey on Embedding Dynamic Graphs—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
A Survey of Knowledge Graph Embedding and Their Applications—0
A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs—0
Robust Graph Embedding with Noisy Link Weights—0
Robust Knowledge Graph Embedding via Denoising—0
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified