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When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods

The is the source code for the paper When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods

Required libraries

You can install the required libraries by running:

pip install -r requirements.txt

How to run the experiments:

You can use the main.py script for running the experiments. Here is the help manual for the script:

Usage: main.py [OPTIONS] EXPERIMENT:[infection|community|saturation]

Arguments:
  EXPERIMENT:[infection|community|saturation]
                                  Dataset to use  [required]

Options:
  --sample-count INTEGER          How many times to retry the whole experiment
                                  [default: 10]

  --num-layers INTEGER            Number of layers in the GNN model  [default: 4]

  --concat-features / --no-concat-features
                                  Concat embeddings of each convolutional
                                  layer for final fc layers  [default: True]

  --conv-type TEXT                Convolution class. Can be GCNConv or
                                  GraphConv  [default: GraphConv]
  --help                          Show this message and exit.

Experiment results in the paper were produced by the following commands:

python main.py infection
python main.py community
python main_node.py saturation --num-layers 1 # for the negative evidence experiment

You can run the Pitfall2-Example.ipynb notebook independently for experimenting with the toy dataset in pitfall 2 explanation.

How to see the results:

Run the mlflow UI by running the following command in the root directory of the project:

mlflow ui

You can view the UI using URL http://localhost:5000. Here is a sample screenshot: