NeurIPS22: CLEAR: Generative Counterfactual Explanations on Graphs

Code for the NeurIPS 2022 paper CLEAR: Generative Counterfactual Explanations on Graphs..

Environment

Python 3.6
Pytorch 1.2.0
Scipy 1.3.1
Numpy 1.17.2
Torch-geometric 1.7.2

Dataset

Datasets can be found in link.

Run Experiment

Step 1: Training a graph prediction model

Train a graph prediction model (i.e., the model which needs explanation). The trained prediction models used in this paper can be directly loaded from ./model_save/.

If you want to train them from scratch, run the following command (here we use the dataset imdb_m as an example):

python train_pred.py  --dataset imdb_m --epochs 600 --lr 0.001

Or you can also use any other graph prediction models instead.

Step 2: Generating counterfactual explanations

python main.py --dataset imdb_m --experiment_type train

Here, when experiment_type is set to train or test, the model CLEAR will be trained or loaded from a saved file. When it is set to baseline, you can run the random perturbation based baselines (INSERT, REMOVE, RANDOM) by setting baseline_type.

Refenrences

The code is the implementation of this paper:

[1] Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li. CLEAR: Generative Counterfactual Explanations on Graphs. Neural Information Processing Systems (NeurIPS), 2022.