MedianGCN

A PyTorch implementation of MedianGCN and TrimmedGCN in Understanding Structural Vulnerability in Graph Convolutional Networks (IJCAI 2021).

Fig. 1. A simple example of the weighted mean, median, and trimmed mean aggregation. The trimmed mean discards the largest and smallest value.

The models are now also available in the package GraphGallery, see:

• graphgallery.gallery.nodeclas.MedianGCN
• graphgallery.gallery.nodeclas.TrimmedGCN

Also, we provided the implementation with PyTorch Geometric (much faster) in DeepRobust, see:

• test_median_gcn

Requirements

• torch>=1.4.0
• graphgallery

git clone https://github.com/EdisonLeeeee/GraphGallery.git && cd GraphGallery
pip install -e . --verbose

Usage

• Performance of our methods compared to GCN before attack (on clean graph): see clean.ipynb
• Performance our methods compared to GCN under Nettack attack (on purtubed graph): see attack.ipynb

Cite

@inproceedings{chen2021understanding,
  title     = {Understanding Structural Vulnerability in Graph Convolutional Networks},
  author    = {Chen, Liang and Li, Jintang and Peng, Qibiao and Liu, Yang and Zheng, Zibin and Yang, Carl},
  booktitle = {IJCAI},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {2249--2255},
  year      = {2021},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2021/310},
  url       = {https://doi.org/10.24963/ijcai.2021/310},
}