SOTAVerified

Graph Embedding

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

( Image credit: GAT )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–575 of 1192 papers

TitleStatusHype
Knowledge Acquisition and Completion for Long-Term Human-Robot Interactions using Knowledge Graph Embedding—0
Domain Representation for Knowledge Graph Embedding—0
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation—0
Knowledge-enriched Two-layered Attention Network for Sentiment Analysis—0
Knowledge Graph Embedding Bi-Vector Models for Symmetric Relation—0
Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models—0
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack—0
Diversified and Adaptive Negative Sampling on Knowledge Graphs—0
Biomedical Network Link Prediction using Neural Network Graph Embedding—0
Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization—0
Biomedical Knowledge Graph Refinement with Embedding and Logic Rules—0
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning—0
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs—0
A Complex Network based Graph Embedding Method for Link Prediction—0
KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding—0
Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy—0
Knowledge Graph Embedding with Hierarchical Relation Structure—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Improving Question Answering over Knowledge Graphs Using Graph Summarization—0
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes—0
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding—0
Binarized Graph Neural Network—0
Kernel classification of connectomes based on earth mover's distance between graph spectra—0
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified