This folder contains the source code used for Structural Message passing for two tasks:

• Cycle detection
• The multi-task regression of graph properties presented in https://arxiv.org/abs/2004.05718

Source code for the second task is adapted from https://github.com/lukecavabarrett/pna.

Pretrained models and more comments will be added in the coming weeks.

[https://pytorch-geometric.readthedocs.io/en/latest/](Pytorch geometric) v1.5.0 was used. Please follow the instructions on the website, as simple installations via pip do not work. In particular, the version of pytorch used must match the one of torch-geometric.

Then install the other dependencies:

pip install -r requirements.txt

First, download the data from https://drive.switch.ch/index.php/s/nwbGJASmgYd0mU6 and unzip it in data/datasets_kcycle_nsamples=10000. Then, run

python3 datasets_generation/build_cycles.py

Simply run

python -m datasets_generation.multitask_dataset

In order to train SMP, specify the cycle length, the size of the graphs that is used, and potentially the proportion of the training data that is kept. For example,

python3 cycle_main.py --k 4 --n 12 --proportion 1.0

will train the 4-cycle on graph with on average 12 nodes on 1.0 * 100 = 100% of the training data.

In order to run another model, modify models.config.yaml. To run a MPNN that has the same architecture as SMP, set use_x=True in this file.

For MPNN and GIN, transforms can be specified in order to add a one-hot encoding of the node degrees, or one-hot identifiers. The available options can be seen by using

python3 cycles_main.py --help

Specify the configuration in the file config_multi_task.yaml, and the the available options by using

python3 multi_task_main.py --help

To use default parameters, simply run:

python3 multi_task_main.py

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