Incremental Training of Graph Neural Networks

Deprecation: Please use https://github.com/lgalke/lifelong-learning instead!

Installation

1. Install pytorch as suited to your OS / python package manager / CUDA version
2. Install dgl as suited to your OS / python package manager / CUDA version
3. Install other requirements via pip install -r requirements.txt within your copy of this repository. This will include mainly pandas and seaborn.

Get the datasets

The three datasets of our paper are available on zenodo. Download the zip files and extract them into the data subdirectory, such that the structure looks exactly like this:

• data/dblp-easy
• data/dblp-hard
• data/pharmabio

Example call to run an experiment

The following exemplary command will run an experiment with a GraphSAGE model (1 hidden layer with 32 hidden units) on the dblp-easy dataset starting evaluation at year 2003 while using 200 annual epochs.

python3 run_experiment.py --seed 42 --model gs-mean --n_hidden 32 --start cold --lr "0.005" --history 3 --n_layers 1 --weight_decay 0 --dropout 0.5 --rescale_lr 1.0 --initial_epochs 0 --annual_epochs 200 --dataset "dblp-easy" --t_start 2003  --save "results.csv"                       

The results.csv file can be reused for multiple runs (e.g. with different seeds, different models, different datasets), the script appends new results to the file. Consult python3 run_experiment.py -h for more information.

Visualize results

You can visualize with the visualize.py script:

python3 visualize.py --style "window size %RF" --hue model --col dataset --row start --nosharey --save plot.png results.csv

where results.csv is the file where you previously aggregated results. You can also provide multiple results files, then they will be concatenated before plotting.

Full reproduction of the paper's experiments

In the experiments/ directory, you find bash scripts to re-run all of our experiments (this may take a while).

File Descriptions

File	Description
analysis	scripts to perform analyses
datasets.py	dataset loading
experiments	scripts to reproduce experiments
models	GNN implementations
README.md	this file
requirements.txt	dependencies
run_experiment.py	main entry point for running a single experiment
tabularize_ecml2020.py	reproduce results table from ECML2020 submission
tabularize.py	aggregate results into table
visualize.py	visualize results