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
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, graph_tool, and igraph packages natively.
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.
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
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