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 876–900 of 1192 papers

TitleStatusHype
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
What the Vec? Towards Probabilistically Grounded Embeddings—0
When Radiology Report Generation Meets Knowledge Graph—0
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network—0
Whom to Learn From? Graph- vs. Text-based Word Embeddings—0
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling—0
Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding—0
Neural IR Meets Graph Embedding: A Ranking Model for Product Search—0
A Relational Tucker Decomposition for Multi-Relational Link Prediction—0
Dynamic Graph Embedding via LSTM History Tracking—0
Dynamic Joint Variational Graph Autoencoders—0
Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image—0
Zero-shot Word Sense Disambiguation using Sense Definition EmbeddingsCode0
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer LearningCode0
Symbolic Graph Embedding using Frequent Pattern MiningCode0
Force-directed graph embedding with hops distanceCode0
3D Rotation and Translation for Hyperbolic Knowledge Graph EmbeddingCode0
A Canonicalization Perspective on Invariant and Equivariant LearningCode0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Active Learning for Graph EmbeddingCode0
A Decade of Scholarly Research on Open Knowledge GraphsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified