Explaining and approximating a graph convolutional network trained on the IMDB-BINARY dataset

The GCN trained on IMDB-BINARY achieves an accuracy of 0.74. Using a custom graph class graph.py to access implicit properties in graphs from the IMDB-BINARY dataset enables an explanation and approximation of the GCN model (with an accuracy of up to 0.84) as well as a direct classification accuracy of 0.71 on the dataset.

Setup

Requirements

This project was run with Python 3.11.3 under Windows 10 (64-bit). Dependencies and exact package versions are listed in requirements.txt.

Installation

1. Install miniconda.
2. Create conda environment with: conda create --name imdb-gcn python=3.11.3
   Note: On Windows you have to enter all of these commands in the Anaconda prompt (see conda docs).
3. Activate conda environment: conda activate imdb-gcn
4. Install PyTorch in activated conda environment. At the time of writing, we used the following command to install PyTorch version 2.0.1:

   conda install pytorch torchvision torchaudio cpuonly -c pytorch
   

5. Install PyTorch Geometric. Note: At the time of writing this, there are issues with installing PyG with conda. We used:

   pip install torch_geometric
   

6. Install pandas: pip install pandas.
7. Install matplotlib:

   python -m pip install -U pip
   python -m pip install -U matplotlib
   

8. Optional: Change to project folder and open environment in Visual Studio Code by typing the command code in the Anaconda Prompt.

Documentation

Results

To replicate our results, run one of the following commands:

1. To see the model accuracy, run: python main.py -evalFinal
2. To see the approximation accuracies, run: python approximation.py

Training

To train a new model, run: python main.py -train

Test

To visualize and/or modify graphs, change working directory to project directory and run: python test.py Visualized graphs are saved under images/graph.png. For more information please read instructions in test.py.

Custom graph class

Opposed to IMDB-BINARY dataset graphs, the custom graphs also contain:

• Node features x containing the degree of nodes.
• Edges edges of shape [2, num_edges].
• A list of movies implicit in the graph. Each movie contains all actors with an edge between each other.
• Functions removeActors(), addActorsToMovie() and removeEdge() to modify graphs. The custom graph class can be used for understanding and explanation of the dataset and/or model.