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In this repository, we provide the codes of SimGRACE to evaluate its performances in terms of generalizability (unsupervised & semi-supervised learning), transferability (transfer learning) and robustness (adversarial robustness).
Dataset download
Semi-supervised learning & Unsupervised representation learning TU Datasets (social and biochemical graphs)
@inproceedings{10.1145/3485447.3512156,
author = {Xia, Jun and Wu, Lirong and Chen, Jintao and Hu, Bozhen and Li, Stan Z.},
title = {SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation},
year = {2022},
isbn = {9781450390965},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3485447.3512156},
doi = {10.1145/3485447.3512156},
booktitle = {Proceedings of the ACM Web Conference 2022},
pages = {1070–1079},
numpages = {10},
keywords = {graph representation learning, contrastive learning, Graph neural networks, robustness, graph self-supervised learning},
location = {Virtual Event, Lyon, France},
series = {WWW '22}
}
Useful resources for Pretrained Graphs Models (PGMs)