SPMGAE: Self-purified masked graph autoencoders release robust expression power

To tackle the scarcity of labeled graph data, graph self-supervised learning (SSL) has branched into two paradigms: Generative methods and Contrastive methods. Inspired by MAE and BERT in computer vision (CV) and natural language processing (NLP), masked graph autoencoders (MGAEs) are gaining popula...

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Vydané v:	Neurocomputing (Amsterdam) Ročník 611; s. 128631
Hlavní autori:	Song, Shuhan, Li, Ping, Dun, Ming, Zhang, Yuan, Cao, Huawei, Ye, Xiaochun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.01.2025
Predmet:	Graph adversarial attacks Graph neural networks Masked graph autoencoders Robustness Masked graph autoencoders Graph neural networks Robustness Graph adversarial attacks
ISSN:	0925-2312
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