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 301–310 of 1192 papers

TitleStatusHype
Multi-Paradigm Analysis of Thai Capital Market Linkages: Bivariate/Vine Copulas, Granger Causality, Network Centrality, and Graph Neural Network/Graph Embedding ApproachesCode0
Refining a k-nearest neighbor graph for a computationally efficient spectral clusteringCode0
Graph Construction using Principal Axis Trees for Simple Graph ConvolutionCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Approximate spectral clustering with eigenvector selection and self-tuned kCode0
Approximate spectral clustering density-based similarity for noisy datasetsCode0
MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis—0
Conformal Prediction for Network-Assisted Regression—0
Persian topic detection based on Human Word association and graph embedding—0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified