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 111–120 of 1192 papers

TitleStatusHype
DUPLEX: Dual GAT for Complex Embedding of Directed GraphsCode0
Efficient Graph Encoder Embedding for Large Sparse Graphs in Python—0
Combinatorial Optimization with Automated Graph Neural NetworksCode2
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Fast and Scalable Multi-Kernel Encoder Classifier—0
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation—0
From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma—0
LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph PredictionsCode0
Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding—0
Valid Conformal Prediction for Dynamic GNNsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified