Here you will find all you need to replicate the experiments in our code (please look at previous releases).
If you happen to use or modify this code, please remember to cite our paper:
Atzeni Daniele, Bacciu Davide, Errica Federico, Micheli Alessio: Modeling Edge Features with Deep Bayesian Graph Networks, IJCNN, 2021.
This repo builds upon PyDGN, a framework to easily develop and test new DGNs. See how to construct your dataset and then train your model there.
This repo assumes PyDGN 1.0.3 is used. Compatibility with future versions is not guaranteed, e.g., custom metrics need to be slightly modified starting from PyDGN 1.2.0.
The evaluation is carried out in two steps:
- Generate the unsupervised graph embeddings
- Apply a classifier on top
We designed two separate experiments to avoid recomputing the embeddings each time. First, use the config_CGMM_Embedding.yml
config file to create the embeddings,
specifying the folder where to store them in the parameter embeddings_folder
. Then, use the config_CGMM_Classifier.yml
config file to launch
the classification experiments.
For instance:
pydgn-dataset --config-file DATA_CONFIGS/config_PROTEINS_custom_transform.yml
pydgn-train --config-file MODEL_CONFIGS/config_ECGMM_Embedding.yml
pydgn-train --config-file MODEL_CONFIGS/config_ECGMM_Classifier.yml