=============================== NetLSD

NetLSD is a family of spectral graph descriptros. Given a graph, NetLSD computes a low-dimensional vector representation that can be used for different tasks.

Quick start

.. code-block:: python

import netlsd
import networkx as nx

g = nx.erdos_renyi_graph(100, 0.01) # create a random graph with 100 nodes
descriptor = netlsd.heat(g) # compute the signature

That's it! Then, signatures of two graphs can be compared easily. NetLSD supports networkx <http://networkx.github.io/>, graph_tool <https://graph-tool.skewed.de/>, and igraph <http://igraph.org/python/>_ packages natively.

.. code-block:: python

import netlsd
import numpy as np

distance = netlsd.compare(desc1, desc2) # compare the signatures using l2 distance
distance = np.linalg.norm(desc1 - desc2) # equivalent

For more advanced usage, check out online documentation <http://netlsd.readthedocs.org/>_.

Requirements

• numpy
• scipy

Installation

#. cd netlsd #. pip install -r requirements.txt #. python setup.py install

Or simply pip install netlsd

Citing

If you find NetLSD useful in your research, we ask that you cite the following paper::

@inproceedings{Tsitsulin:2018:KDD,
 author={Tsitsulin, Anton and Mottin, Davide and Karras, Panagiotis and Bronstein, Alex and M{\"u}ller, Emmanuel},
 title={NetLSD: Hearing the Shape of a Graph},
 booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '18},
 year = {2018},
} 

Misc

NetLSD - Hearing the shape of graphs.

• MIT license
• Documentation: http://netlsd.readthedocs.org