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 331–340 of 1192 papers

TitleStatusHype
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation—0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—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
A Canonicalization Perspective on Invariant and Equivariant LearningCode0
Causal-aware Graph Neural Architecture Search under Distribution Shifts—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified