https://github.com/danielzuegner/robust-gcn
Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".
https://github.com/danielzuegner/robust-gcn
adversarial-robust deep-learning graph-neural-networks robustness
Last synced: 7 months ago
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Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".
- Host: GitHub
- URL: https://github.com/danielzuegner/robust-gcn
- Owner: danielzuegner
- License: mit
- Created: 2019-06-21T12:05:44.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-12-07T15:16:05.000Z (over 5 years ago)
- Last Synced: 2024-11-28T02:35:25.442Z (over 1 year ago)
- Topics: adversarial-robust, deep-learning, graph-neural-networks, robustness
- Language: Jupyter Notebook
- Homepage: https://www.kdd.in.tum.de/research/robust-gcn/
- Size: 6.1 MB
- Stars: 42
- Watchers: 2
- Forks: 12
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Certifiable Robustness and Robust Training for GCN
Implementation of the paper:
**[Certifiable Robustness and Robust Training for Graph Convolutional Networks](https://arxiv.org/abs/1906.12269)**
by Daniel Zügner and Stephan Günnemann.
Published at KDD'19, August 2019, Anchorage, USA
Copyright (C) 2019
Daniel Zügner
Technical University of Munich
## Additional resources
[[Paper](https://arxiv.org/pdf/1906.12269.pdf) | [Poster](https://www.in.tum.de/fileadmin/w00bws/daml/robust-gcn/robust_gcn_poster.pdf) | [Slides (KDD 2019)](https://www.in.tum.de/fileadmin/w00bws/daml/robust-gcn/kdd2019_presentation_flattened.pdf)]
## Run the code
The fastest way to try our code is to use the Jupyter notebook `demo.ipynb`.
## Requirements
* Python 3.6 or newer
* `numpy`
* `scipy`
* `scikit-learn`
* `pytorch`
* `matplotlib` (for the demo notebook)
`tqdm` is recommended for displaying progress bars.
## Installation
`python setup.py install`
If you just want to add a symbolic link to your package directory run
`python setup.py develop`
## Contact
Please contact zuegnerd@in.tum.de in case you have any questions.
## References
### Datasets
In the `data` folder we provide the following datasets originally published by
#### Cora
McCallum, Andrew Kachites, Nigam, Kamal, Rennie, Jason, and Seymore, Kristie.
*Automating the construction of internet portals with machine learning.*
Information Retrieval, 3(2):127–163, 2000.
and the graph was extracted by
Bojchevski, Aleksandar, and Stephan Günnemann. *"Deep gaussian embedding of
attributed graphs: Unsupervised inductive learning via ranking."* ICLR 2018.
#### Citeseer
Sen, Prithviraj, Namata, Galileo, Bilgic, Mustafa, Getoor, Lise, Galligher, Brian, and Eliassi-Rad, Tina.
*Collective classification in network data.*
AI magazine, 29(3):93, 2008.
#### PubMed
Sen, Prithviraj, Namata, Galileo, Bilgic, Mustafa, Getoor, Lise, Galligher, Brian, and Eliassi-Rad, Tina.
*Collective classification in network data.*
AI magazine, 29(3):93, 2008.
### Graph Convolutional Networks
Our implementation of the GCN algorithm is based on the authors' implementation,
available on GitHub [here](https://github.com/tkipf/gcn).
The paper was published as
Thomas N Kipf and Max Welling. 2017.
*Semi-supervised classification with graph
convolutional networks.* ICLR (2017).
## Cite
Please cite our paper if you use the model or this code in your own work:
```
@inproceedings{zugner2019robustgcn,
title={Certifiable Robustness and Robust Training for Graph Convolutional Networks},
author={Z{\"u}gner, Daniel and G{\"u}nnemann, Stephan},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \&\#38; Data Mining},
year={2019},
publisher = {ACM},
address = {New York, NY, USA},
location = {Anchorage, United States},
}
```