Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation

Deep graph neural networks (GNNs) have been shown to be expressive for modeling graph-structured data. Nevertheless, the over-stacked architecture of deep graph models makes it difficult to deploy and rapidly test on mobile or embedded systems. To compress over-stacked GNNs, knowledge distillation v...

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Hauptverfasser:	He, Huarui, Wang, Jie, Zhang, Zhanqiu, Wu, Feng
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning
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