Merge pull request #41 from pnnl/develop

Develop

Author: brendapraggastis

README.md

@@ -33,23 +33,20 @@ Tutorials may be run in your browser using Google Colab
 <a href="https://colab.research.google.com/github/pnnl/HyperNetX/blob/master/tutorials/Tutorial%202%20-%20Visualization%20Methods.ipynb" target="_blank">
 
 
-
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 	<span style={'margin-left':5px}>Tutorial 2 - Visualization Methods</span>
 </a>
 </br>
 <a href="https://colab.research.google.com/github/pnnl/HyperNetX/blob/master/tutorials/Tutorial%203%20-%20LesMis%20Case%20Study.ipynb" target="_blank">
 
 
-
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 	<span style={'margin-left':5px}>Tutorial 3 - LesMis Case Study</span>
 </a>
 </br>
 <a href="https://colab.research.google.com/github/pnnl/HyperNetX/blob/master/tutorials/Tutorial%204%20-%20LesMis%20Visualizations-BookTour.ipynb" target="_blank">
 
 
-
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 	<span style={'margin-left':5px}>Tutorial 4 - LesMis Visualizations-Book Tour</span>
 </a>

docs/build/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 100d5f0571659fa080bb0f6ef7eebc72
+config: 12513d66143dfa27e7a4e78bbb5b3696
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/build/.doctrees/algorithms/algorithms.doctree

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>algorithms.homology_mod2 &mdash; HyperNetX 0.3.4 documentation</title>
+  <title>algorithms.homology_mod2 &mdash; HyperNetX 0.3.5 documentation</title>
 
 
 

docs/build/.doctrees/environment.pickle

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>algorithms.s_centrality_measures &mdash; HyperNetX 0.3.4 documentation</title>
+  <title>algorithms.s_centrality_measures &mdash; HyperNetX 0.3.5 documentation</title>
 
 
 
@@ -161,7 +161,22 @@
   <h1>Source code for algorithms.s_centrality_measures</h1><div class="highlight"><pre>
 <span></span><span class="c1"># Copyright © 2018 Battelle Memorial Institute</span>
 <span class="c1"># All rights reserved.</span>
-<span class="c1"># This module developed for PNNL&#39;s hyperbio LDRD project summer 2019</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">S-Centrality Measures</span>
+<span class="sd">=====================</span>
+<span class="sd">We generalize graph metrics to s-metrics for a hypergraph by using its s-connected</span>
+<span class="sd">components. This is accomplished by computing the s edge-adjacency matrix and</span>
+<span class="sd">constructing the corresponding graph of the matrix. We then use existing graph metrics</span>
+<span class="sd">on this representation of the hypergraph. In essence we construct an *s*-line graph</span>
+<span class="sd">corresponding to the hypergraph on which to apply our methods.</span>
+
+<span class="sd">S-Metrics for hypergraphs are discussed in depth in:        </span>
+<span class="sd">*Aksoy, S.G., Joslyn, C., Ortiz Marrero, C. et al. Hypernetwork science via high-order hypergraph walks.</span>
+<span class="sd">EPJ Data Sci. 9, 16 (2020). https://doi.org/10.1140/epjds/s13688-020-00231-0*</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">defaultdict</span>
@@ -193,10 +208,12 @@ <h1>Source code for algorithms.s_centrality_measures</h1><div class="highlight">
 <span class="sd">        A dictionary of s-betweenness centrality value of the edges.</span>
 
 <span class="sd">    &#39;&#39;&#39;</span>
+
     <span class="c1"># Confirm there is at least 1 s edge for which we can compute the centrality</span>
     <span class="c1"># Find all s-edges</span>
     <span class="c1">#M,rdict,_ = H.incidence_matrix(index=True)</span>
     <span class="c1">#A = M.transpose().dot(M)</span>
+
     <span class="n">A</span><span class="p">,</span> <span class="n">coldict</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">edge_adjacency_matrix</span><span class="p">(</span><span class="n">s</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">A</span><span class="o">.</span><span class="n">setdiag</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
     <span class="n">A</span> <span class="o">=</span> <span class="p">(</span><span class="n">A</span> <span class="o">&gt;=</span> <span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1</span>
@@ -227,8 +244,10 @@ <h1>Source code for algorithms.s_centrality_measures</h1><div class="highlight">
 <span class="sd">        If edge then a single value is returned.</span>
 
 <span class="sd">    &#39;&#39;&#39;</span>
+
     <span class="c1"># Confirm there is at least 1 s edge for which we can compute the centrality</span>
     <span class="c1"># Find all s-edges</span>
+
     <span class="k">if</span> <span class="n">edge</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">H</span><span class="o">.</span><span class="n">edges</span><span class="p">[</span><span class="n">edge</span><span class="p">])</span> <span class="o">&lt;</span> <span class="n">s</span><span class="p">:</span>
         <span class="k">return</span> <span class="mi">0</span>
 
@@ -250,6 +269,7 @@ <h1>Source code for algorithms.s_centrality_measures</h1><div class="highlight">
 
     <span class="c1"># confirm there are at least 2 s-edges</span>
     <span class="c1"># we follow the NX convention that the s-closeness centrality of a single edge Hypergraph is 0</span>
+
     <span class="n">output</span> <span class="o">=</span> <span class="p">{}</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">bool</span><span class="p">(</span><span class="n">Es</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">Es</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
         <span class="n">output</span> <span class="o">=</span> <span class="p">{</span><span class="n">e</span><span class="p">:</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">}</span>
@@ -280,7 +300,11 @@ <h1>Source code for algorithms.s_centrality_measures</h1><div class="highlight">
 <span class="sd">        eccentricity[f] : float</span>
 <span class="sd">    else:</span>
 <span class="sd">        eccentricity_dict : dict</span>
-<span class="sd">        </span>
+<span class="sd">     : dict or float</span>
+<span class="sd">        returns the s-eccentricity value of the edges, a floating point number</span>
+<span class="sd">        If edge=None a dictionary of values for each s-edge in H is returned.</span>
+<span class="sd">        If edge then a single value is returned.</span>
+
 <span class="sd">    &#39;&#39;&#39;</span>
     <span class="k">if</span> <span class="n">f</span><span class="p">:</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">Entity</span><span class="p">):</span>

docs/build/_modules/algorithms/homology_mod2.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>classes.entity &mdash; HyperNetX 0.3.4 documentation</title>
+  <title>classes.entity &mdash; HyperNetX 0.3.5 documentation</title>
 
 
 

docs/build/_modules/algorithms/s_centrality_measures.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>classes.hypergraph &mdash; HyperNetX 0.3.4 documentation</title>
+  <title>classes.hypergraph &mdash; HyperNetX 0.3.5 documentation</title>
 
 
 

docs/build/_modules/classes/entity.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>drawing.rubber_band &mdash; HyperNetX 0.3.4 documentation</title>
+  <title>drawing.rubber_band &mdash; HyperNetX 0.3.5 documentation</title>
 
 
 

docs/build/_modules/classes/hypergraph.html

@@ -8,7 +8,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>drawing.two_column &mdash; HyperNetX 0.3.4 documentation</title>
+  <title>drawing.two_column &mdash; HyperNetX 0.3.5 documentation</title>