DFG-NAS: Deep and Flexible Graph Neural Architecture Search

1 code implementation • 17 Jun 2022 • Wentao Zhang, Zheyu Lin, Yu Shen, Yang Li, Zhi Yang, Bin Cui

Graph neural networks (GNNs) have been intensively applied to various graph-based applications.

Neural Architecture Search Node Classification

PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm

1 code implementation • 1 Mar 2022 • Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui

Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs.

Neural Architecture Search

GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing

no code implementations • 20 Apr 2021 • Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui

In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks.