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Adversarial Attacks on Graph Neural Networks via Meta Learning

Implementation of the paper:
Adversarial Attacks on Graph Neural Networks via Meta Learning

by Daniel Zügner and Stephan Günnemann.
Published at ICLR'19, May 2019, New Orleans, USA

Requirements

Python 3.6 or newer
numpy
scipy
scikit-learn
tensorflow
matplotlib (for the demo notebook)
seaborn (for the demo notebook)

Installation

python setup.py install

Run the code

To try our code, you can use the IPython notebook demo.ipynb.

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.

PolBlogs

Lada A Adamic and Natalie Glance. 2005. The political blogosphere and the 2004
US election: divided they blog.
In Proceedings of the 3rd international workshop on Link discovery. 36–43.

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{zugner_adversarial_2019,
	title = {Adversarial Attacks on Graph Neural Networks via Meta Learning},
	author={Z{\"u}gner, Daniel and G{\"u}nnemann, Stephan},
	booktitle={International Conference on Learning Representations (ICLR)},
	year = {2019}
}