1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
|
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>community API — Community detection for NetworkX 2 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/classic.css" type="text/css" />
<script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script>
<script src="_static/underscore.js"></script>
<script src="_static/doctools.js"></script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="prev" title="Community detection for NetworkX’s documentation" href="index.html" />
<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
try{
var pageTracker = _gat._getTracker("UA-7251741-1");
pageTracker._trackPageview();
} catch(err) {}
</script>
</head><body>
<div style="background-color: white; text-align: left; padding: 10px 10px 15px 15px">
<a href="index.html">Community detection</a>
</div>
<div class="related" role="navigation" aria-label="related navigation">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="genindex.html" title="General Index"
accesskey="I">index</a></li>
<li class="right" >
<a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="right" >
<a href="index.html" title="Community detection for NetworkX’s documentation"
accesskey="P">previous</a> |</li>
<li><a href="index.html">home</a>| </li>
<li><a href="search.html">search</a>| </li>
<li><a href="#">documentation </a> »</li>
<li class="nav-item nav-item-this"><a href="">community API</a></li>
</ul>
</div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper">
<h3><a href="index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">community API</a></li>
<li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
</ul>
<h4>Previous topic</h4>
<p class="topless"><a href="index.html"
title="previous chapter">Community detection for NetworkX’s documentation</a></p>
<div role="note" aria-label="source link">
<h3>This Page</h3>
<ul class="this-page-menu">
<li><a href="_sources/api.rst.txt"
rel="nofollow">Show Source</a></li>
</ul>
</div>
<div id="searchbox" style="display: none" role="search">
<h3 id="searchlabel">Quick search</h3>
<div class="searchformwrapper">
<form class="search" action="search.html" method="get">
<input type="text" name="q" aria-labelledby="searchlabel" />
<input type="submit" value="Go" />
</form>
</div>
</div>
<script>$('#searchbox').show(0);</script>
</div>
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="community-api">
<h1>community API<a class="headerlink" href="#community-api" title="Permalink to this headline">¶</a></h1>
<div class="toctree-wrapper compound">
</div>
<span class="target" id="module-community"></span><p>This package implements community detection.</p>
<p>Package name is community but refer to python-louvain on pypi</p>
<dl class="py function">
<dt id="community.best_partition">
<code class="sig-prename descclassname">community.</code><code class="sig-name descname">best_partition</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph</span></em>, <em class="sig-param"><span class="n">partition</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">weight</span><span class="o">=</span><span class="default_value">'weight'</span></em>, <em class="sig-param"><span class="n">resolution</span><span class="o">=</span><span class="default_value">1.0</span></em>, <em class="sig-param"><span class="n">randomize</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#community.best_partition" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the partition of the graph nodes which maximises the modularity
(or try..) using the Louvain heuristices</p>
<p>This is the partition of highest modularity, i.e. the highest partition
of the dendrogram generated by the Louvain algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>graph</strong><span class="classifier">networkx.Graph</span></dt><dd><p>the networkx graph which is decomposed</p>
</dd>
<dt><strong>partition</strong><span class="classifier">dict, optional</span></dt><dd><p>the algorithm will start using this partition of the nodes.
It’s a dictionary where keys are their nodes and values the communities</p>
</dd>
<dt><strong>weight</strong><span class="classifier">str, optional</span></dt><dd><p>the key in graph to use as weight. Default to ‘weight’</p>
</dd>
<dt><strong>resolution</strong><span class="classifier">double, optional</span></dt><dd><p>Will change the size of the communities, default to 1.
represents the time described in
“Laplacian Dynamics and Multiscale Modular Structure in Networks”,
R. Lambiotte, J.-C. Delvenne, M. Barahona</p>
</dd>
<dt><strong>randomize</strong><span class="classifier">boolean, optional</span></dt><dd><p>Will randomize the node evaluation order and the community evaluation
order to get different partitions at each call</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, optional (default=None)</span></dt><dd><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <cite>np.random</cite>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>partition</strong><span class="classifier">dictionnary</span></dt><dd><p>The partition, with communities numbered from 0 to number of communities</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><dl class="simple">
<dt>NetworkXError</dt><dd><p>If the graph is not Eulerian.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="#community.generate_dendrogram" title="community.generate_dendrogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_dendrogram</span></code></a></dt><dd><p>to obtain all the decompositions levels</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>Uses Louvain algorithm</p>
<p class="rubric">References</p>
<p>large networks. J. Stat. Mech 10008, 1-12(2008).</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># basic usage</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">community</span> <span class="k">as</span> <span class="nn">community_louvain</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">erdos_renyi_graph</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">partion</span> <span class="o">=</span> <span class="n">community_louvain</span><span class="o">.</span><span class="n">best_partition</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># display a graph with its communities:</span>
<span class="gp">>>> </span><span class="c1"># as Erdos-Renyi graphs don't have true community structure,</span>
<span class="gp">>>> </span><span class="c1"># instead load the karate club graph</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">community</span> <span class="k">as</span> <span class="nn">community_louvain</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">karate_club_graph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="c1"># compute the best partition</span>
<span class="gp">>>> </span><span class="n">partition</span> <span class="o">=</span> <span class="n">community_louvain</span><span class="o">.</span><span class="n">best_partition</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># draw the graph</span>
<span class="gp">>>> </span><span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">spring_layout</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># color the nodes according to their partition</span>
<span class="gp">>>> </span><span class="n">cmap</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">'viridis'</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">partition</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">partition</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">partition</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_edges</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="community.generate_dendrogram">
<code class="sig-prename descclassname">community.</code><code class="sig-name descname">generate_dendrogram</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph</span></em>, <em class="sig-param"><span class="n">part_init</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">weight</span><span class="o">=</span><span class="default_value">'weight'</span></em>, <em class="sig-param"><span class="n">resolution</span><span class="o">=</span><span class="default_value">1.0</span></em>, <em class="sig-param"><span class="n">randomize</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#community.generate_dendrogram" title="Permalink to this definition">¶</a></dt>
<dd><p>Find communities in the graph and return the associated dendrogram</p>
<p>A dendrogram is a tree and each level is a partition of the graph nodes.
Level 0 is the first partition, which contains the smallest communities,
and the best is len(dendrogram) - 1. The higher the level is, the bigger
are the communities</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>graph</strong><span class="classifier">networkx.Graph</span></dt><dd><p>the networkx graph which will be decomposed</p>
</dd>
<dt><strong>part_init</strong><span class="classifier">dict, optional</span></dt><dd><p>the algorithm will start using this partition of the nodes. It’s a
dictionary where keys are their nodes and values the communities</p>
</dd>
<dt><strong>weight</strong><span class="classifier">str, optional</span></dt><dd><p>the key in graph to use as weight. Default to ‘weight’</p>
</dd>
<dt><strong>resolution</strong><span class="classifier">double, optional</span></dt><dd><p>Will change the size of the communities, default to 1.
represents the time described in
“Laplacian Dynamics and Multiscale Modular Structure in Networks”,
R. Lambiotte, J.-C. Delvenne, M. Barahona</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>dendrogram</strong><span class="classifier">list of dictionaries</span></dt><dd><p>a list of partitions, ie dictionnaries where keys of the i+1 are the
values of the i. and where keys of the first are the nodes of graph</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><dl class="simple">
<dt>TypeError</dt><dd><p>If the graph is not a networkx.Graph</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="#community.best_partition" title="community.best_partition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">best_partition</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>Uses Louvain algorithm</p>
<p class="rubric">References</p>
<p>networks. J. Stat. Mech 10008, 1-12(2008).</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">erdos_renyi_graph</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">dendo</span> <span class="o">=</span> <span class="n">generate_dendrogram</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">level</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dendo</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="p">:</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="s2">"partition at level"</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s2">"is"</span><span class="p">,</span> <span class="n">partition_at_level</span><span class="p">(</span><span class="n">dendo</span><span class="p">,</span> <span class="n">level</span><span class="p">))</span>
<span class="go">:param weight:</span>
<span class="go">:type weight:</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="community.induced_graph">
<code class="sig-prename descclassname">community.</code><code class="sig-name descname">induced_graph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">partition</span></em>, <em class="sig-param"><span class="n">graph</span></em>, <em class="sig-param"><span class="n">weight</span><span class="o">=</span><span class="default_value">'weight'</span></em><span class="sig-paren">)</span><a class="headerlink" href="#community.induced_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Produce the graph where nodes are the communities</p>
<p>there is a link of weight w between communities if the sum of the weights
of the links between their elements is w</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>partition</strong><span class="classifier">dict</span></dt><dd><p>a dictionary where keys are graph nodes and values the part the node
belongs to</p>
</dd>
<dt><strong>graph</strong><span class="classifier">networkx.Graph</span></dt><dd><p>the initial graph</p>
</dd>
<dt><strong>weight</strong><span class="classifier">str, optional</span></dt><dd><p>the key in graph to use as weight. Default to ‘weight’</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>g</strong><span class="classifier">networkx.Graph</span></dt><dd><p>a networkx graph where nodes are the parts</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">n</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">>>> </span><span class="n">g</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_graph</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">part</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([])</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">g</span><span class="o">.</span><span class="n">nodes</span><span class="p">()</span> <span class="p">:</span>
<span class="gp">>>> </span> <span class="n">part</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="n">node</span> <span class="o">%</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">ind</span> <span class="o">=</span> <span class="n">induced_graph</span><span class="p">(</span><span class="n">part</span><span class="p">,</span> <span class="n">g</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">goal</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">goal</span><span class="o">.</span><span class="n">add_weighted_edges_from</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="o">*</span><span class="n">n</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="n">n</span><span class="o">*</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">*</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)])</span> <span class="c1"># NOQA</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">is_isomorphic</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">goal</span><span class="p">)</span>
<span class="go">True</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="community.load_binary">
<code class="sig-prename descclassname">community.</code><code class="sig-name descname">load_binary</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em><span class="sig-paren">)</span><a class="headerlink" href="#community.load_binary" title="Permalink to this definition">¶</a></dt>
<dd><p>Load binary graph as used by the cpp implementation of this algorithm</p>
</dd></dl>
<dl class="py function">
<dt id="community.modularity">
<code class="sig-prename descclassname">community.</code><code class="sig-name descname">modularity</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">partition</span></em>, <em class="sig-param"><span class="n">graph</span></em>, <em class="sig-param"><span class="n">weight</span><span class="o">=</span><span class="default_value">'weight'</span></em><span class="sig-paren">)</span><a class="headerlink" href="#community.modularity" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the modularity of a partition of a graph</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>partition</strong><span class="classifier">dict</span></dt><dd><p>the partition of the nodes, i.e a dictionary where keys are their nodes
and values the communities</p>
</dd>
<dt><strong>graph</strong><span class="classifier">networkx.Graph</span></dt><dd><p>the networkx graph which is decomposed</p>
</dd>
<dt><strong>weight</strong><span class="classifier">str, optional</span></dt><dd><p>the key in graph to use as weight. Default to ‘weight’</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>modularity</strong><span class="classifier">float</span></dt><dd><p>The modularity</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><dl class="simple">
<dt>KeyError</dt><dd><p>If the partition is not a partition of all graph nodes</p>
</dd>
<dt>ValueError</dt><dd><p>If the graph has no link</p>
</dd>
<dt>TypeError</dt><dd><p>If graph is not a networkx.Graph</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<p>structure in networks. Physical Review E 69, 26113(2004).</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">community</span> <span class="k">as</span> <span class="nn">community_louvain</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">erdos_renyi_graph</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">partition</span> <span class="o">=</span> <span class="n">community_louvain</span><span class="o">.</span><span class="n">best_partition</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">modularity</span><span class="p">(</span><span class="n">partition</span><span class="p">,</span> <span class="n">G</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="community.partition_at_level">
<code class="sig-prename descclassname">community.</code><code class="sig-name descname">partition_at_level</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dendrogram</span></em>, <em class="sig-param"><span class="n">level</span></em><span class="sig-paren">)</span><a class="headerlink" href="#community.partition_at_level" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the partition of the nodes at the given level</p>
<p>A dendrogram is a tree and each level is a partition of the graph nodes.
Level 0 is the first partition, which contains the smallest communities,
and the best is len(dendrogram) - 1.
The higher the level is, the bigger are the communities</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>dendrogram</strong><span class="classifier">list of dict</span></dt><dd><p>a list of partitions, ie dictionnaries where keys of the i+1 are the
values of the i.</p>
</dd>
<dt><strong>level</strong><span class="classifier">int</span></dt><dd><p>the level which belongs to [0..len(dendrogram)-1]</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>partition</strong><span class="classifier">dictionnary</span></dt><dd><p>A dictionary where keys are the nodes and the values are the set it
belongs to</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><dl class="simple">
<dt>KeyError</dt><dd><p>If the dendrogram is not well formed or the level is too high</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="#community.best_partition" title="community.best_partition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">best_partition</span></code></a></dt><dd><p>which directly combines partition_at_level and</p>
</dd>
<dt><a class="reference internal" href="#community.generate_dendrogram" title="community.generate_dendrogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_dendrogram</span></code></a></dt><dd><p>to obtain the partition of highest modularity</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">erdos_renyi_graph</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">dendrogram</span> <span class="o">=</span> <span class="n">generate_dendrogram</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">level</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dendrogram</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="p">:</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="s2">"partition at level"</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="s2">"is"</span><span class="p">,</span> <span class="n">partition_at_level</span><span class="p">(</span><span class="n">dendrogram</span><span class="p">,</span> <span class="n">level</span><span class="p">))</span> <span class="c1"># NOQA</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
</ul>
</div>
<div class="clearer"></div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
<div class="related" role="navigation" aria-label="related navigation">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="genindex.html" title="General Index"
>index</a></li>
<li class="right" >
<a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="right" >
<a href="index.html" title="Community detection for NetworkX’s documentation"
>previous</a> |</li>
<li><a href="index.html">home</a>| </li>
<li><a href="search.html">search</a>| </li>
<li><a href="#">documentation </a> »</li>
<li class="nav-item nav-item-this"><a href="">community API</a></li>
</ul>
</div>
<div class="footer" role="contentinfo">
© Copyright 2010, Thomas Aynaud.
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 3.4.1.
</div>
<div class="footer">This page uses <a href="http://analytics.google.com/">
Google Analytics</a> to collect statistics. You can disable it by blocking
the JavaScript coming from www.google-analytics.com.
<script type="text/javascript">
(function() {
var ga = document.createElement('script');
ga.src = ('https:' == document.location.protocol ?
'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
ga.setAttribute('async', 'true');
document.documentElement.firstChild.appendChild(ga);
})();
</script>
</div>
</body>
</html>
|
