Dall’Amico, Barrat, Cattuto
Welcome!#
This is the documentation for the GDyanDist Python package, which enables the computation of distances between pairs of temporal graphs. For further information, please refer to the article.
Installation#
You can install the GDynaDist package using pip by running the following command in the terminal.
pip install gdynadist
We also shared an Anaconda environment in which all codes were run and tested. You can create it by running the following commands in the terminal
conda env create -f EDRep_env.yml
conda activate EDRep
Content#
In the following pages, we describe the main features implemented in this package and provide some use examples.
Dependency on EDRep#
The GDynaDist package relies on the EDRep algorithm to compute temporal graph embeddings. Here, the user can find the related paper and package documentation.
| Documentation | Paper |
Citation#
If you make use of these codes, please use the following citations:
This is the Nature Communications article in which the distance was proposed.
@article{dallamico2024embeddingbased,
title={An embedding-based distance for temporal graphs},
volume={15},
ISSN={2041-1723},
url={http://dx.doi.org/10.1038/s41467-024-54280-4},
DOI={10.1038/s41467-024-54280-4},
number={1},
journal={Nature Communications},
publisher={Springer Science and Business Media LLC},
author={Dall’Amico, Lorenzo and Barrat, Alain and Cattuto, Ciro},
year={2024},
month=nov }
This is the TMLR paper in which we developed the EDRep algorithm on which the distance definition relies.
@article{
dall'amico2025learning,
title={Learning distributed representations with efficient SoftMax normalization},
author={Lorenzo Dall'Amico and Enrico Maria Belliardo},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=9M4NKMZOPu},
note={}
}