IDEA

This repository is our Pytorch implementation of our paper:

IDEA: Invariant Causal Learning for Graph Adversarial Robustness against Attacks Submitted to Information Sciences

Datasets and splits

• Download Cora, Citeseer, ogbarxiv, ogbproducts (the subgraph in our paper), Reddit (the subgraph in our paper) from Here.

• Download the train/val/test splits mentioned in our paper are also included in the above link. Please note that, for Cora and Citeseer, we adopt the commonly used splits which are included in datasets/Cora/ and datasets/Citeseer/.

Unzip the idea_Data_Split.zip, and put the two folders (datasets and splits) in this directory.

Attacked graphs

Download the attacked graphs used in our paper from Here.

Unzip attacked_graph.zip, and put the folder attacked_graphs in this directory.

Environment

• python >= 3.9
• pytorch == 1.11.0--cuda11.3
• scipy == 1.9.3
• numpy == 1.23.5
• deeprobust
• ogb

Reproduce the results

We provide to evaluate the robustness of IDEA by poisoning and evasion attacks.

• Poisoning attack (MetaAttack in our paper)

  Example Usage

  python -u main.py --dataset cora --alpha 100  --dom_num 10  --device 1

• Evasion attack (nettack, PGD, G-NIA, TDGIA in our paper)

  Example Usage

  python -u test.py --dataset cora

Running scripts and parameters for all the datasets are given in run.sh