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<h1>Source code for networkx.algorithms.vitality</h1><div class="highlight"><pre>
<span class="sd">"""</span>
<span class="sd">Vitality measures.</span>
<span class="sd">"""</span>
<span class="c"># Copyright (C) 2012 by</span>
<span class="c"># Aric Hagberg <hagberg@lanl.gov></span>
<span class="c"># Dan Schult <dschult@colgate.edu></span>
<span class="c"># Pieter Swart <swart@lanl.gov></span>
<span class="c"># All rights reserved.</span>
<span class="c"># BSD license.</span>
<span class="kn">import</span> <span class="nn">networkx</span> <span class="kn">as</span> <span class="nn">nx</span>
<span class="n">__author__</span> <span class="o">=</span> <span class="s">"</span><span class="se">\n</span><span class="s">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s">'Aric Hagberg (hagberg@lanl.gov)'</span><span class="p">,</span>
<span class="s">'Renato Fabbri'</span><span class="p">])</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s">'closeness_vitality'</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">weiner_index</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="c"># compute sum of distances between all node pairs</span>
<span class="c"># (with optional weights)</span>
<span class="n">weiner</span><span class="o">=</span><span class="mf">0.0</span>
<span class="k">if</span> <span class="n">weight</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
<span class="n">path_length</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">single_source_shortest_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span><span class="n">n</span><span class="p">)</span>
<span class="n">weiner</span><span class="o">+=</span><span class="nb">sum</span><span class="p">(</span><span class="n">path_length</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
<span class="n">path_length</span><span class="o">=</span><span class="n">nx</span><span class="o">.</span><span class="n">single_source_dijkstra_path_length</span><span class="p">(</span><span class="n">G</span><span class="p">,</span>
<span class="n">n</span><span class="p">,</span><span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
<span class="n">weiner</span><span class="o">+=</span><span class="nb">sum</span><span class="p">(</span><span class="n">path_length</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="k">return</span> <span class="n">weiner</span>
<div class="viewcode-block" id="closeness_vitality"><a class="viewcode-back" href="../../../reference/generated/networkx.algorithms.vitality.closeness_vitality.html#networkx.algorithms.vitality.closeness_vitality">[docs]</a><span class="k">def</span> <span class="nf">closeness_vitality</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Compute closeness vitality for nodes.</span>
<span class="sd"> Closeness vitality of a node is the change in the sum of distances</span>
<span class="sd"> between all node pairs when excluding that node.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> G : graph</span>
<span class="sd"> weight : None or string (optional)</span>
<span class="sd"> The name of the edge attribute used as weight. If None the edge</span>
<span class="sd"> weights are ignored.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> nodes : dictionary</span>
<span class="sd"> Dictionary with nodes as keys and closeness vitality as the value.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G=nx.cycle_graph(3)</span>
<span class="sd"> >>> nx.closeness_vitality(G)</span>
<span class="sd"> {0: 4.0, 1: 4.0, 2: 4.0}</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> closeness_centrality()</span>
<span class="sd"> References</span>
<span class="sd"> ----------</span>
<span class="sd"> .. [1] Ulrik Brandes, Sec. 3.6.2 in</span>
<span class="sd"> Network Analysis: Methodological Foundations, Springer, 2005.</span>
<span class="sd"> http://books.google.com/books?id=TTNhSm7HYrIC</span>
<span class="sd"> """</span>
<span class="n">multigraph</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">is_multigraph</span><span class="p">()</span>
<span class="n">wig</span> <span class="o">=</span> <span class="n">weiner_index</span><span class="p">(</span><span class="n">G</span><span class="p">,</span><span class="n">weight</span><span class="p">)</span>
<span class="n">closeness_vitality</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
<span class="c"># remove edges connected to node n and keep list of edges with data</span>
<span class="c"># could remove node n but it doesn't count anyway</span>
<span class="k">if</span> <span class="n">multigraph</span><span class="p">:</span>
<span class="n">edges</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span><span class="n">keys</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span>
<span class="n">edges</span> <span class="o">+=</span> <span class="n">G</span><span class="o">.</span><span class="n">in_edges</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span><span class="n">keys</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">edges</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">if</span> <span class="n">G</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span>
<span class="n">edges</span> <span class="o">+=</span> <span class="n">G</span><span class="o">.</span><span class="n">in_edges</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">G</span><span class="o">.</span><span class="n">remove_edges_from</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span>
<span class="n">closeness_vitality</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">wig</span> <span class="o">-</span> <span class="n">weiner_index</span><span class="p">(</span><span class="n">G</span><span class="p">,</span><span class="n">weight</span><span class="p">)</span>
<span class="c"># add edges and data back to graph</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span>
<span class="k">return</span> <span class="n">closeness_vitality</span></div>
</pre></div>
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