Deep learning 2019 group work on graph pooling methods

Installation

You can create the environment using conda: conda env create -f environment.yml

However, we use pytorch geometric and it can be difficult to install. We would recommend that you create an environment without pytorch-geometric and then install it following the official instructions here

Run the code

The core of our project are three experiments.

• complexity: Here we tested the memory requirements, the time complexity and sample efficiency of the different pooling methods.
• datasets: Here we tested the performance of the different pooling methods on different datasets.
• varying_training_percentage: Here we tested the performance of the different datasets on Cora as a function of the training label percentage.

Every experiment has its proper folder in the experiments folder (e.g., experiments/datasets). Every experiment contains a config file and a main file. In order to reproduce the experiments it is enough to just run the mainfile. However, due to personal preference of the authors the experiments have different working folders.

Here are the files by experiment:

• complexity
  • config: complexity_config.py
  • main: complexity_run.py
  • wdir: .../DL2019_Graphpooling
  • plots: complexity_plots.py
• datasets:
  • config: datasets_config.py
  • main: datasets_run.py.py
  • wdir: .../DL2019_Graphpooling
• varying_training_percentage
  • config: varying_training_percentage_config.py
  • main: varying_training_percentage_experiments.py
  • wdir: .../DL2019_Graphpooling/experiments/
  • plots: integrated in main file varying_training_percentage

Feel free to play around with the config file to generate alternative experiments.

Other

For GPU support replace cpuonly with cudatoolkit=10.1 using your cuda version. You can run a motivating example using binder

Authors

Henry Martin, Christian Bohn, Lili Georgieva