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 441–450 of 1192 papers

TitleStatusHype
GPINN: Physics-informed Neural Network with Graph EmbeddingCode0
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generatorsCode0
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional RotationCode0
Dissimilar Nodes Improve Graph Active LearningCode0
An Investigation of Evaluation Metrics for Automated Medical Note GenerationCode0
Binarized Attributed Network EmbeddingCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
GraphZoom: A multi-level spectral approach for accurate and scalable graph embeddingCode0
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity PredictionCode0
Billion-scale Commodity Embedding for E-commerce Recommendation in AlibabaCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified