1. Ben Edwards
  2. networkx-bedwards

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Aric Hagberg  committed 3d769be

Update documentation in overview and front page.

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File doc/source/contents.rst

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     :Release: |version|
     :Date: |today|
 
-    Download 
-    :download:`PDF Tutorial <networkx_tutorial.pdf>`
-    :download:`PDF Reference <networkx_reference.pdf>`
-    :download:`HTML <networkx-documentation.zip>`
+    :download:`Tutorial (PDF)  <networkx_tutorial.pdf>`
+
+    :download:`Reference (PDF)  <networkx_reference.pdf>`
+
+    :download:`Tutorial+Reference (HTML zip)  <networkx-documentation.zip>`
 
 .. toctree::
    :maxdepth: 2

File doc/source/overview.rst

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 ..  -*- coding: utf-8 -*-
 
-Introduction
-============
+Overview
+========
 
-NetworkX is a Python-based package for the creation, manipulation, and
-study of the structure, dynamics, and function of complex networks.
+NetworkX is a Python language software package for the creation, 
+manipulation, and study of the structure, dynamics, and function of complex networks.  
 
-The structure of a graph or network is encoded in the **edges**
-(connections, links, ties, arcs, bonds) between **nodes** (vertices,
-sites, actors). If unqualified, by graph we mean an undirected
-graph, i.e. no multiple edges are allowed. By a network we usually 
-mean a graph with weights (fields, properties) on nodes and/or edges.
+With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure,  build network models, design new network algorithms, draw networks, and much more.
+
 
 Who uses NetworkX?
 ------------------
 
 The potential audience for NetworkX includes mathematicians,
-physicists, biologists, computer scientists, and social scientists. The
-current state of the art of the science of
-complex networks is presented in Albert and Barabási [BA02]_, Newman
+physicists, biologists, computer scientists, and social scientists.  Good 
+reviews of the state-of-the-art in the science of
+complex networks are presented in Albert and Barabási [BA02]_, Newman
 [Newman03]_, and Dorogovtsev and Mendes [DM03]_. See also the classic
 texts [Bollobas01]_, [Diestel97]_ and [West01]_ for graph theoretic
 results and terminology. For basic graph algorithms, we recommend the
 texts of Sedgewick, e.g. [Sedgewick01]_ and [Sedgewick02]_ and the
 survey of Brandes and Erlebach [BE05]_.
   
+Goals
+-----
+NetworkX is intended to provide
+
+-  tools for the study the structure and
+   dynamics of social, biological, and infrastructure networks,
+
+-  a standard programming interface and graph implementation that is suitable
+   for many applications, 
+
+-  a rapid development environment for collaborative, multidisciplinary
+   projects,
+
+-  an interface to existing numerical algorithms and code written in C, 
+   C++, and FORTRAN, 
+
+-  the ability to painlessly slurp in large nonstandard data sets. 
+
+
 The Python programming language
 -------------------------------
 
-Why Python? Past experience showed this approach to maximize
-productivity, power, multi-disciplinary scope (applications include large communication, social, data and biological
-networks), and platform independence. This philosophy does not exclude
-using whatever other language is appropriate for a specific subtask,
-since Python is also an excellent "glue" language [Langtangen04]_. 
-Equally important, Python is free, well-supported and a joy to use. 
+Python is a powerful programming language that allows simple and flexible representations of networks, and  clear and concise expressions of network algorithms (and other algorithms too).  Python has a vibrant and growing ecosystem of packages that NetworkX uses to provide more features such as numerical linear algebra and drawing.  In addition 
+Python is also an excellent "glue" language for putting together pieces of software from other languages which allows reuse of legacy code and engineering of high-performance algorithms [Langtangen04]_. 
+
+Equally important, Python is free, well-supported, and a joy to use. 
+
+In order to make the most out of NetworkX you will want to know how to write basic programs in Python.  
 Among the many guides to Python, we recommend the documentation at
 http://www.python.org and the text by Alex Martelli [Martelli03]_.
 
 -------------
 
 NetworkX is free software; you can redistribute it and/or
-modify it under the terms of the :doc:`NetworkX License </reference/legal>`.
+modify it under the terms of the :doc:`BSD License </reference/legal>`.
 We welcome contributions from the community.  Information on
 NetworkX development is found at the NetworkX Developer Zone
 https://networkx.lanl.gov/trac.
 
-Goals
------
-NetworkX is intended to:
-
--  Be a tool to study the structure and
-   dynamics of social, biological, and infrastructure networks
-
--  Provide ease-of-use and rapid
-   development in a collaborative, multidisciplinary environment 
-
--  Be an Open-source software package that can provide functionality
-   to a diverse community of active and easily participating users
-   and developers. 
-
--  Provide an easy interface to 
-   existing code bases written in C, C++, and FORTRAN 
-
--  Painlessly slurp in large nonstandard data sets 
-
--  Provide a standard API and/or graph implementation that is 
-   suitable for many applications. 
 
 History
 -------
 
--  NetworkX was inspired by Guido van Rossum's 1998 Python 
-   graph representation essay [vanRossum98]_.
+NetworkX was born in May 2002. The original version was designed and written by Aric Hagberg, Dan Schult, and Pieter Swart in 2002 and 2003.  
+The first public release was in April 2005.
 
--  First public release in April 2005.  Version 1.0 released in 2009.
+Many people have contributed to the success of NetworkX.   Some of the contributors are listed in the :doc:`credits. </reference/credits>`
+
 
 
 What Next

File doc/source/reference/credits.rst

View file
 Credits
 -------
 
-NetworkX was originally written by Aric Hagberg, Dan Schult, and Pieter Swart
-with the help of many others.   
+NetworkX was originally written by Aric Hagberg, Dan Schult, and Pieter Swart,
+and has been developed with the help of many others.   
 
 Thanks to Guido van Rossum for the idea of using Python for
 implementing a graph data structure

File doc/source/reference/introduction.rst

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-Overview
-~~~~~~~~
+Introduction
+~~~~~~~~~~~~
 .. currentmodule:: networkx
 
 .. only:: html

File doc/source/templates/index.html

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 <h2>High productivity software for complex networks</a></h2>
 
 <blockquote>
-<p>NetworkX is a Python package for the creation, manipulation, and
+<p>NetworkX is a Python language software package for the creation, manipulation, and
 study of the structure, dynamics, and functions of complex networks.</p>
 </blockquote>
 
 <table class="contentstable" align="center" style="margin-left: 30px"><tr>
   <td width="50%">
 
+    <p class="biglink"><a class="biglink" href="{{ pathto("overview") }}">Overview</a><br/>
+       <span class="linkdescr">the big picture</span></p>
+
     <p class="biglink"><a class="biglink" href="{{ pathto("tutorial/index") }}">Tutorial</a><br/>
-       <span class="linkdescr">start here</span></p>
+       <span class="linkdescr">get started here</span></p>
     <p class="biglink"><a class="biglink" href="{{ pathto("reference/index") }}">Reference</a><br/>
        <span class="linkdescr">guide to all functions and classes</span></p>
+  </td>
+  <td width="50%">
+    <p class="biglink"><a class="biglink" href="{{ pathto("contents") }}">Contents</a><br/>
+       <span class="linkdescr">all documentation</span></p>
     <p class="biglink"><a class="biglink" href="{{ pathto("examples/index") }}">Examples</a><br/>
        <span class="linkdescr">using the library</span></p>
     <p class="biglink"><a class="biglink" href="{{ pathto("gallery") }}">Gallery</a><br/>
        <span class="linkdescr">network drawings</span></p>
-  </td>
-  <td width="50%">
-    <p class="biglink"><a class="biglink" href="{{ pathto("contents") }}">Contents</a><br/>
-       <span class="linkdescr">a complete overview</span></p>
-    <p class="biglink"><a class="biglink" href="{{ pathto("search") }}">Search Page</a><br/>
-       <span class="linkdescr">search the documentation</span></p>
-    <p class="biglink"><a class="biglink" href="{{ pathto("genindex") }}">General Index</a><br/>
-       <span class="linkdescr">all functions, classes, terms</span></p>
-    <p class="biglink"><a class="biglink" href="{{ pathto("modindex") }}">Module Index</a><br/>
-       <span class="linkdescr">quick access to all documented modules</span></p>
   </td></tr>
 </table>
 
 <h2>Features</h2>
 <ul class="simple">
-<li> Standard graph-theoretic and statistical physics functions</li>
-<li>Easy exchange of network algorithms between applications,
-disciplines, and platforms</li>
-<li> Many classic graphs and synthetic networks</li>
-<li> Nodes and edges can be &quot;anything&quot;
-(e.g. time-series, text, images, XML records)</li>
-
-<li>Exploits existing code from high-quality legacy software in C,
-C++, Fortran, etc.</li>
-<li>Open source   <a href="reference/legal.html">BSD license</a> </li>
-<li>More than 1000 unit tests</li>
+<li> Python language data structures for graphs, digraphs, and multigraphs.
+<li> Nodes can be &quot;anything&quot; (e.g. text, images, XML records)</li>
+<li> Edges can hold arbitrary data (e.g. weights, time-series) 
+<li> Generators for classic graphs, random graphs, and synthetic networks</li>
+<li> Standard graph algorithms</li>
+<li> Network structure and analysis measures</li>
+<li> Basic graph drawing</li>
+<li> Open source <a href="reference/legal.html">BSD license</a> </li>
+<li> Well tested: more than 1500 unit tests</li>
+<li> Additional benefits from Python: fast prototyping, easy to teach, multi-platform</li>
 </ul>
 
-<p><em>Additional benefits from Python</em></p>
-
-<ul class="simple">
-<li>Fast prototyping of new algorithms</li>
-<li>Easy to teach</li>
-<li>Multi-platform</li>
-
-<li>Allows easy access to almost any database</li>
-</ul>
+<p><em></em></p>
 
 
 {% endblock %}