المفاهيم الأساسية
The author proposes a three-stage multitask distillation framework to address the challenges of positional information loss and low generalization in teaching student MLPs on graphs.
الملخص
The content discusses the problems faced in inference tasks on large-scale graph datasets, introduces a new framework for knowledge distillation, and presents experimental results demonstrating the effectiveness of the proposed approach. Key points include addressing positional information loss, utilizing hidden layer distillation, and improving performance on various benchmark datasets.
الإحصائيات
A frequently used Twitter-7 dataset contains over seventeen million nodes and over four hundred million edges.
The proposed framework outperforms existing state-of-the-art methods on various benchmark datasets.