https://github.com/xgfs/NetLSD

NetLSD descriptors for graphs. Compare and analyze graph structure on multiple levels!
https://github.com/xgfs/NetLSD

Last synced: 19 days ago
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NetLSD descriptors for graphs. Compare and analyze graph structure on multiple levels!

• Host: GitHub
• URL: https://github.com/xgfs/NetLSD
• Owner: xgfs
• License: mit
• Created: 2018-06-04T14:55:57.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-02-26T18:46:44.000Z (over 4 years ago)
• Last Synced: 2024-09-18T05:02:07.880Z (2 months ago)
• Language: Python
• Homepage:
• Size: 22.5 KB
• Stars: 59
• Watchers: 8
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • Contributing: docs/contributing.rst
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-graph-classification - [Python Reference

README

        
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NetLSD

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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 `_, `graph_tool `_, and `igraph `_ 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 `_.

Requirements

------------

* numpy

* scipy

Installation

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#. cd netlsd

#. pip install -r requirements.txt 

#. python setup.py install

Or simply ``pip install netlsd``

Citing

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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

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NetLSD - Hearing the shape of graphs.

* MIT license

* Documentation: http://netlsd.readthedocs.org