SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

Fuente: arXiv

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Bibliographic Details
Main Authors:	Wu, Qitian, Zhao, Wentao, Yang, Chenxiao, Zhang, Hengrui, Nie, Fan, Jiang, Haitian, Bian, Yatao, Yan, Junchi
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Social and Information Networks
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2306.10759