Relating graph auto-encoders to linear models

Code

All functionality from training is implemented (and well commented) in the gnn folder.

run_batch.py

• if you run it locally and for testing, you can specify parameters in the hard coded dictionary at the top of the file and run it as main
  • alternatively give as arguments via command line
  • refer parser.py function for default configurations of all parameters
• to run several tasks on the server I created params files with all arguments for a single run in one line.
• output is automatically written to ./outputs containing the parameter configuration, training and testing results.

scripts

• calls the run_batch.py method on every parameter configuration given in textform

• all files contain absolute paths and need to be personalized

• Example for citeseer:

cd ..

while read p; do
	singularity exec --nv /YOUR_SINGULARITY_IMAGE.simg python3 PATH_TO/run_batch.py $p
	echo ONE MORE DONE!
done <~/PATH_TO_THE_PARAMETERS/experiments_citeseer.txt

echo ALL DONE!

params

• .txt files containing one parameterconfiguration per line
• existing files contain experiments from paper

Figures

• all figures from the paper can be automatically created via the figures.py
• it will create .tex output but you can adapt the ending to generate .pdf or .png files