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 1176–1192 of 1192 papers

TitleStatusHype
Grassmann Graph Embedding—0
GREG: A Global Level Relation Extraction with Knowledge Graph Embedding—0
Group Representation Theory for Knowledge Graph Embedding—0
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning—0
Spectral Augmentations for Graph Contrastive Learning—0
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation—0
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network—0
Detecting text level intellectual influence with knowledge graph embeddings—0
Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task—0
HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification—0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning—0
Heterogeneous Graph Neural Network with Multi-view Representation Learning—0
Heterogeneous manifolds for curvature-aware graph embedding—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks—0
Spectral Embedding of Graph Networks—0
Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified