Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson 0001, Furong Huang, Tom Goldstein. VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. pages 6733-6746, 2021. [doi]
@inproceedings{DingKLZDHG21,
title = {VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization},
author = {Mucong Ding and Kezhi Kong and Jingling Li and Chen Zhu and John Dickerson 0001 and Furong Huang and Tom Goldstein},
year = {2021},
url = {https://proceedings.neurips.cc/paper/2021/hash/3569df159ec477451530c4455b2a9e86-Abstract.html},
researchr = {https://researchr.org/publication/DingKLZDHG21},
cites = {0},
citedby = {0},
pages = {6733-6746},
booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual},
editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan},
}