NoisyGL Documentation
NoisyGL is a benchmark for Graph Neural Networks under Label Noise (GLN). It provides a fair and comprehensive platform to evaluate existing LLN and GLN works and facilitate future GLN research.
Note
This project is under active development.
Citation
If our work could help your research, we would appreciate citations to the paper:
@inproceedings{NEURIPS2024_436ffa18,
author = {Wang, Zhonghao and Sun, Danyu and Zhou, Sheng and Wang, Haobo and Fan, Jiapei and Huang, Longtao and Bu, Jiajun},
booktitle = {Advances in Neural Information Processing Systems},
editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
pages = {38142--38170},
publisher = {Curran Associates, Inc.},
title = {NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise},
url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/436ffa18e7e17be336fd884f8ebb5748-Paper-Datasets_and_Benchmarks_Track.pdf},
volume = {37},
year = {2024}
}
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