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 871–880 of 1192 papers

TitleStatusHype
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks—0
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning—0
WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows—0
Walk this Way! Entity Walks and Property Walks for RDF2vec—0
Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings—0
Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding—0
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization—0
Weight Prediction for Variants of Weighted Directed Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified