graph_autoencoder

kaggle2021-11-08 更新2024-03-08 收录

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2021-11-08

Adversarially Regularized Graph Autoencoder for Graph Embedding

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structur

DataCite Commons2026-01-07 更新00

Processed Score Graph Dataset for the Score Autoencoder (score-ae)

This is a processed version of the PDMX dataset (deduplicated and no license conflict) using GraphMuse Library. This dataset is used by the score-ae repository:https://github.com/hendrik-roth/score-

Zenodo2026-03-12 更新00

SpaMask: Dual Masking Graph Autoencoder with Contrastive Learning for Spatial Transcriptomics

Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled

Zenodo2024-11-13 更新00

DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder

Graph neural networks (GNNs) achieve remarkable performance for tasks on graph data. However, recent works show they are extremely vulnerable to adversarial structural perturbations, making their outc

DataCite Commons2025-01-03 更新50

Processed Score Graph Dataset for the Score Autoencoder (score-ae)

This is a processed version of the PDMX dataset (deduplicated and no license conflict) using GraphMuse Library. This dataset is used by the score-ae repository:https://github.com/hendrik-roth/score-

Zenodo2026-03-12 更新00