.. _sphx_glr_gallery_plot_use_attributes_centrality.py:


GraVE Documentation
-------------------





.. image:: /gallery/images/sphx_glr_plot_use_attributes_centrality_001.png
    :align: center





.. code-block:: python

    import networkx as nx
    from networkx.algorithms.centrality import closeness_centrality
    import matplotlib.pyplot as plt

    from grave import plot_network, use_attributes

    toy_network = nx.barbell_graph(10, 14)
    toy_centrality = closeness_centrality(toy_network)
    max_centrality = max(toy_centrality.values())

    for u, v, edge_attributes in toy_network.edges.data():
        c = (toy_centrality[u] +
             toy_centrality[v]) / 2
        color_idx = (c / max_centrality)
        cmap = plt.get_cmap()
        edge_attributes['color'] = cmap(color_idx)
        edge_attributes['width'] = 2

    for node, node_attributes in toy_network.nodes.data():
        node_attributes['size'] = (1 - (toy_centrality[node] / 
                                    max_centrality) + .1) * 100


    def edge_style(edge_attributes):
        return {'linewidth': edge_attributes.get('weight', 1)}

    fig, ax = plt.subplots()
    plot_network(toy_network,
                 layout='spring',
                 node_style=use_attributes(),
                 edge_style=use_attributes('color'))
    plt.show()

**Total running time of the script:** ( 0 minutes  0.154 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_use_attributes_centrality.py <plot_use_attributes_centrality.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_use_attributes_centrality.ipynb <plot_use_attributes_centrality.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_