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Project description

Sonnet J(SON + Net)workX

Sonnet wraps a NetworkX graph and produces detailed JSON output for use with JavaScript to produce detailed graph visualizations in the browser.

Getting Started

Install Sonnet::

pip install sonnet

Build a NetworkX graph::

import networkx as nx

g = nx.gnp_random_graph(200, 0.5)

Wrap it with Sonnet::

import sonnet as sn

s = sn.Sonnet(g)

Build stats directly into node directory using modified NetworkX algorithms. Currently available: degree, degree_centrality, in_degree_centrality, out_degree_centrality, closeness_centrality, betweenness_centrality, eigenvector_centrality::



[{'betweenness_centrality': 0.2222222222222222, 'id': 0},
{'betweenness_centrality': 0.2152777777777778, 'id': 1},
{'betweenness_centrality': 0.006944444444444444, 'id': 2},
{'betweenness_centrality': 0.39814814814814814, 'id': 3},
{'betweenness_centrality': 0.11805555555555555, 'id': 4},
{'betweenness_centrality': 0.020833333333333332, 'id': 5},
{'betweenness_centrality': 0.06944444444444445, 'id': 6},
{'betweenness_centrality': 0.0, 'id': 7},
{'betweenness_centrality': 0.018518518518518517, 'id': 8},
{'betweenness_centrality': 0.041666666666666664, 'id': 9}]

Find communities and assign nodes to group based on community::


Rank node size by nodes by attribute::


Now we have a nodes with lots of relevant data::

Produce JSON data::

json_graph = s.jsonify()


D3Graph is designed to produce JSON output for D3.js graphs. It works just like Sonnet, but it has extra attributes set at during init.


s = sn.Sonnet()


d = ns.D3Graph()


Project details

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sonnet-0.1.0.tar.gz (7.3 kB view hashes)

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