Metadata-Version: 1.1
Name: NetLSD
Version: 1.0.1
Summary: NetLSD descriptors for graphs. Compare and analyze graph structure on multiple levels!
Home-page: http://github.com/xgfs/netlsd
Author: Anton Tsitsulin
Author-email: anton.tsitsulin@hpi.de
License: MIT
Download-URL: https://github.com/xgfs/netlsd/archive/0.1.tar.gz
Description: ===============================
        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
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
