semvec/pypi-packages huggingface dataset:
GDynaDist 0.1.1
GDynaDist: an embedding-based distance for temporal graphs
Requires Python: >=3.9

# G-DynaDist

### Dall'Amico, Barrat, Cattuto - *An embedding-based distance for temporal graphs*

| **[Documentation](https://lorenzodallamico.github.io/G-DynaDist/intro.html)** 
| **[Paper](https://www.nature.com/articles/s41467-024-54280-4)** | 

```bash
pip install gdynadist
```


## Summary

These codes implement our definition of distance between temporal graphs as defined in [Dall'Amico, Cattuto, Barrat *An embedding-based distance for temporal graphs*](https://www.nature.com/articles/s41467-024-54280-4). We consider the problem of comparing pairs of temporal graphs, *i.e* relational datasets in which the interaction between node pairs $i, j$ are time-stamped.

We define two metrics for `matched` and `unmatched` graphs, respectively. We say two graphs are *matched* if there exists a known bijective relationship between the nodes of the two graphs and they *unmatched* otherwise. Matched graphs have the same number of nodes and represent different instances of the same graph, while unmatched graphs may have a different number of nodes.

To compute the distance, we first obtain a node embedding of the temporal graphs, and then we compute the distances in the embedded space. The embedding is obtained using the [EDRep](https://github.com/lorenzodallamico/EDRep) Python package. The figure below summarizes the pipeline adopted to compute the distance.

![](Fig/pipeline.png)

## Citation

If you make use of these codes, please reference the following articles

```
@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 }
```

```
@article{
dallamico2025learning,
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={}
}
```
    
## Author

[Lorenzo Dall'Amico](https://lorenzodallamico.github.io/) - lorenzo.dallamico@isi.it

## Licence

This software is released under the GNU AFFERO GENERAL PUBLIC LICENSE (see included file LICENSE)