Certifiable Robustness and Robust Training for GCN

Implementation of the paper:
Certifiable Robustness and Robust Training for Graph Convolutional Networks

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 | Poster | Slides (KDD 2019)]

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 [email protected] 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.

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},
}