GLEE is a graph embedding technique based on the spectral and geometric properties of the Laplacian matrix of an undirected graph. For more details on the theoretical framework behind GLEE, see
Leo Torres, K. S. Chan, and T. Eliassi-Rad. GLEE: Geometric Laplacian Eigenmap Embedding. Journal of Complex Networks, Volume 8, Issue 2, April 2020, cnaa007.
If you use the code in this repository, please cite the above article.
Clone this repo to access the GLEE functionality. Requirements are numpy, scipy and networkX.
This library can be used in two different ways. It can be directly imported
for use within your Python application using import glee. For an example
of this kind of usage see GLEE example usage.ipynb.
It can also be used from the command line by executing python glee.py <args>. Running python glee.py -h will show an explanation of the
available arguments.
usage: glee.py [-h] --input INPUT --output OUTPUT [--dim DIM]
[--method METHOD]
optional arguments:
-h, --help show this help message and exit
--input INPUT edge list file path
--output OUTPUT embedding output file path
--dim DIM dimension of embedding (default 128)
--method METHOD "glee" or "eigen"
A usual way to use GLEE in this way is, for example,
python glee.py --input karate.txt --output karate.out --dim 8
One can also compute the original version of Laplacian Eigenmaps by doing
python glee.py --input karate.txt --output karate.out --dim 8 --method eigen