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

  >>> from neuro_umap import nbeigs_calculate, distance_gr_wass

  • Example:

  >>> eigs=nbeigs_calculate(graphs,'2D')
  >>> distance_gr_wass(eigs)

References

Motivated on the following articles:

• Torres, L., Suárez-Serrato, P. & Eliassi-Rad, T.
  Non-backtracking Cycles: Length Spectrum Theory and Graph Mining Applications,
  Appl Netw Sci 4, 41 (2019)

• Achard, S., Delon-Martin, C., et al.,
  Hubs of brain functional networks are radically reorganized in comatose patients,
  PNAS 109, 50 (2012)