Decoupled graph knowledge distillation: A general logits-based method for learning MLPs on graphs

While Graph Neural Networks (GNNs) have demonstrated their effectiveness in processing non-Euclidean structured data, the neighborhood fetching of GNNs is time-consuming and computationally intensive, making them difficult to deploy in low-latency industrial applications. To address the issue, a fea...

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Veröffentlicht in:	Neural networks 2024-11, Vol.179, p.106567, Article 106567
Hauptverfasser:	Tian, Yingjie, Xu, Shaokai, Li, Muyang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Decoupling Graph knowledge distillation Graph neural networks Knowledge Logistic Models Machine Learning Multi-layer perceptrons Neural Networks, Computer
Online-Zugang:	Volltext
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