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License: Creative Commons Zero v1.0 Universal
Adversarial attacks and defenses on Graph Neural Networks.
License: Creative Commons Zero v1.0 Universal
Hi Dr. Wei Jin,
Thanks for maintaining this awesome repo!
I would like to add our work (as follows) to this repo, which is quite relevant.
Thanks for your attention.
Best Regards,
Zhanke
Hi,
it would be great if you could list our recent workshop paper:
That would be awesome! Thanks in advance and for maintaining this helpful overview!
@ChandlerBang
Hi there,
i have a task that i want to solve using machine learning,
are you interested in freelancing?
what is your email so that i can discuss with you with details,
waiting for your reply
Hi @ChandlerBang,
It will be great if you can add our latest graph backdoor paper accepted by RAID 2022,
"Transferable Graph Backdoor Attack. Shuiqiao Yang, Bao Gia Doan, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe, Damith C. Ranasinghe, Salil S. Kanhere"
URL: https://arxiv.org/abs/2207.00425
Thanks.
Covering two scalable attacks (both local/global or I think you call it targeted/untargeted) and one defense.
https://www.in.tum.de/daml/robustness-of-gnns-at-scale/
@inproceedings{geisler2021_robustness_of_gnns_at_scale,
title = {Robustness of Graph Neural Networks at Scale},
author = {Tobias Schmidt, Hakan \c{S}irin, DanielZ"ugner, Aleksandar Bojchevski, and StephanG"unnemann},
booktitle={Neural Information Processing Systems, {NeurIPS}},
year = {2021}
}
Adversarial attacks on neural solvers for TSP and SAT.
https://arxiv.org/abs/2110.10942
Thanks :-)
Hi! Thanks a lot for curating this very helpful collection of graph adversarial robustness papers.
I wanted to ask if you could add the following two certificate papers from our group (the second one can be applied to various tasks, but is especially effective for graph neural networks).
https://www.cs.cit.tum.de/daml/interception-smoothing
@inproceedings{scholten2022interception_smoothing,
title = {Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks},
author = {Scholten, Yan and Schuchardt, Jan and Geisler, Simon and Bojchevski, Aleksandar and G{\"u}nnemann, Stephan},
booktitle={Neural Information Processing Systems, {NeurIPS}},
year = {2022}
}
and
https://openreview.net/forum?id=-k7Lvk0GpBl
@inproceedings{schuchardt2023localized_smoothing,
title = {Localized Randomized Smoothing for Collective Robustness Certification},
author = {Schuchardt, Jan and Wollschl\"ager, Tom and Bojchevski, Aleksandar and G{\"u}nnemann, Stephan},
booktitle={International Conference on Learning Representations, {ICLR}},
year = {2023}
}
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