DGI: An Easy and Efficient Framework for GNN Model Evaluation-Reference-Cited by-同舟云学术

DGI: An Easy and Efficient Framework for GNN Model Evaluation

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Yin Peiqi¹^ORCID,Yan Xiao²^ORCID,Zhou Jinjing³^ORCID,Fu Qiang⁴^ORCID,Cai Zhenkun⁵^ORCID,Cheng James⁶^ORCID,Tang Bo²^ORCID,Wang Minjie⁵^ORCID

Affiliation:

1. AWS Shanghai AI Lab, Southern University of Science and Technology, & The Chinese University of Hong Kong, Shanghai, China

2. Southern University of Science and Technology, Shenzhen, China

3. TensorChord, Shanghai, China

4. George Washington University, Washington, DC, USA

5. AWS Shanghai AI Lab, Shanghai, China

6. The Chinese University of Hong Kong, Shatin, Hong Kong

Funder

Guangdong Basic and Applied Basic Research Foundation

CUHK

Shenzhen Fundamental Research Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599805

Reference64 articles.

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2. Sami Abu-El-Haija , Bryan Perozzi , Amol Kapoor , Nazanin Alipourfard , Kristina Lerman , Hrayr Harutyunyan , Greg Ver Steeg , and Aram Galstyan . 2019 . Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing . In international conference on machine learning. PMLR, 21--29 . Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning. PMLR, 21--29.

3. Rabbit Order: Just-in-Time Parallel Reordering for Fast Graph Analysis

4. DGCL

5. DSP

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