https://github.com/haydeeperuyero/coned-backtracking-distance-between-graphs
https://github.com/haydeeperuyero/coned-backtracking-distance-between-graphs
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: springer.com -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (6.2%) to scientific vocabulary
Last synced: 6 months ago
·
JSON representation
Repository
Basic Info
- Host: GitHub
- Owner: HaydeePeruyero
- Default Branch: master
- Size: 39.1 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of psuarezserrato/Coned-backtracking-distance-between-graphs
Created about 4 years ago
· Last pushed about 4 years ago
https://github.com/HaydeePeruyero/Coned-backtracking-distance-between-graphs/blob/master/
# Graph distance
Calculate distance between graphs. The following distances are supported:
| Distance | Description |
|:-------------------------:|:----------------------------------------------------------------------------------------:|
| spectral | This is the original python sunbeam distance |
| wasserstein_kde_dist | Wasserstein distance between estimated distributions of nonbacktracking eigenvalues |
| distance_gr_wass | Gromov-Wasserstein distance between nonbacktracking eigenvalue vectors |
## Running code
Python version >= 3.5
* __Run on your local machine__
* Clone this repository on your local machine. `git clone https://github.com/liubaoryol/graph_distance.git`
* Install requirements: `pip install -r requirements.txt`
* Open a terminal with the path where you cloned this repository `C:Users/desktop/graph_distance$ python`
* Import `neuro_umap` functions as follows
```bash
>>> from neuro_umap import nbeigs_calculate, distance_gr_wass
```
* Example:
```bash
>>> eigs=nbeigs_calculate(graphs,'2D')
>>> distance_gr_wass(eigs)
```
## References
Motivated on the following articles:
* Torres, L., Surez-Serrato, P. & Eliassi-Rad, T.
[Non-backtracking Cycles: Length Spectrum
Theory and Graph Mining Applications](https://link.springer.com/article/10.1007/s41109-019-0147-y),
Appl Netw Sci 4, 41 (2019)
* Achard, S., Delon-Martin, C., et al.,
[Hubs of brain functional networks are radically
reorganized in comatose patients](https://www.pnas.org/content/109/50/20608),
PNAS 109, 50 (2012)
Owner
- Name: Haydee Peruyero
- Login: HaydeePeruyero
- Kind: user
- Location: México
- Website: https://haydeeperuyero.github.io/
- Repositories: 23
- Profile: https://github.com/HaydeePeruyero
Posdoctorante en el Centro de Ciencias Matemáticas UNAM. Doctorado en Ciencias Matemáticas, IMATE UNAM. Especialidad en Estadística Aplicada, IIMAS UNAM.