01_tools.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "output-file: tools.html\n",
    "title: Graphs in Python\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebooks describes various tools that can be used to work with graphs in Python.\n",
    "\n",
    "The [Network Chart - The Python Graph Gallery](https://python-graph-gallery.com/network-chart/)\n",
    "uses mainly [NetworkX](https://networkx.github.io/documentation/stable/index.html) library\n",
    "for showing examples of [network diagrams / charts](https://www.data-to-viz.com/graph/network.html) (or graphs),\n",
    "but also mentions [Graph Tool](https://graph-tool.skewed.de/) library\n",
    "for consideration, especially concerning high dimension networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List of considered libraries**\n",
    "\n",
    "- [**NetworkX**](https://networkx.org/documentation/stable/index.html)\n",
    "(and [cuGraph](https://github.com/rapidsai/cugraph) from [RAPIDS](https://rapids.ai/))\n",
    "- [Graph Tool](https://graph-tool.skewed.de/)\n",
    "- [Snap.py](http://snap.stanford.edu/snappy/index.html) interface to [SNAP](http://snap.stanford.edu/snap/) (Stanford Network Analysis Platform)\n",
    "- [NetworKit](https://networkit.github.io/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python graph libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![networkx-logo](https://networkx.org/_static/networkx_logo.svg)\n",
    "\n",
    "- [NetworkX library](https://networkx.org/documentation/stable/index.html) -- data structures for graphs (or networks) along with graph algorithms, generators, and drawing tools\n",
    "  - Data structures for graphs, digraphs, and multigraphs\n",
    "  - Many standard graph algorithms\n",
    "  - Network structure and analysis measures\n",
    "  - Nodes can be \"anything\" (e.g., text, images, XML records),  \n",
    "    Edges can hold arbitrary data (e.g., weights, time-series)\n",
    "\n",
    "  \n",
    "- [cuGraph](https://github.com/rapidsai/cugraph) - GPU Graph Analytics\n",
    "  - [RAPIDS](https://rapids.ai/) Graph Analytics Library\n",
    "  - Process data found in [GPU DataFrames (cuDF)](https://github.com/rapidsai/cudf), with the Pandas-like API\n",
    "  - NetworkX-like API to allow existing code to be ported with minimal effort into RAPIDS\n",
    "  - _current limitations:_ Vertex IDs are expected to be contiguous integers starting from 0 (renumbering can be done automatically)  \n",
    "    the renumbered vertex IDs need to be representable in 32-bit integers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![graph-tool-logo](https://graph-tool.skewed.de/static/img/graph-tool-logo.svg)**graph-tool**\n",
    "\n",
    "- [Graph Tool library](https://graph-tool.skewed.de/) -- the core data structures and algorithms are implemented in C++\n",
    "  - Based heavily on the [Boost Graph C++ Library](https://www.boost.org/doc/libs/release/libs/graph)\n",
    "  - Many algorithms are implemented in parallel using OpenMP\n",
    "  - Support for arbitrary vertex, edge or graph properties\n",
    "  - An extensive array of features is included, such as graph statistics, centrality measures, standard topological algorithms\n",
    "  - Graph-tool has its own layout algorithms and versatile, interactive drawing routines based on cairo and GTK+, but it can also work as a very comfortable interface to the excellent [graphviz](https://www.graphviz.org/) package\n",
    "\n",
    "Drawing in Jupyter Notebooks described e.g. in https://gist.github.com/jg-you/2ce69ffccf6f3ed17c09e5d7b2695f1c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![snap-logo](http://snap.stanford.edu/images/snap_logo.png)\n",
    "\n",
    "- [Snap.py](http://snap.stanford.edu/snappy/index.html) is a Python interface for SNAP (Stanford Network Analysis Platform)\n",
    "  - [SNAP](http://snap.stanford.edu/snap/) is a general purpose, high performance system in C++ for analysis and manipulation of large networks\n",
    "  - Most of the SNAP functionality is available via Snap.py in Python\n",
    "  - It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges\n",
    "  - Nodes, edges and attributes in a graph or a network can be changed dynamically during the computation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"background-color: black;\" src=\"https://networkit.github.io/_static/logo_bw_inv.png\" alt=\"networkit-logo\" width=\"30%\"/>\n",
    "\n",
    "- [NetworKit](https://networkit.github.io/) is a growing [open-source toolkit](https://github.com/kit-parco/networkit) for large-scale network analysis.\n",
    "  - High-performance algorithms are written in C++ and exposed to Python via the Cython toolchain\n",
    "  - Documented in [NetworKit UserGuide](https://github.com/kit-parco/networkit/blob/Dev/notebooks/User-Guide.ipynb) notebook\n",
    "  - Seamless integration with Python libraries for scientific computing and data analysis, e.g.\n",
    "    - pandas for data framework processing and analytics,\n",
    "    - matplotlib for plotting,\n",
    "    - networkx for additional network analysis tasks, or\n",
    "    - numpy and scipy for numerical and scientific computing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ![igraph-logo](https://igraph.org/img/igraph_logo_black.svg) -->\n",
    "<img src=\"https://igraph.org/img/igraph_logo_black.svg\" width=\"20%\" />\n",
    "\n",
    "- [iGraph](https://igraph.org/) is a collection of network analysis tools with the emphasis on **efficiency**, **portability** and ease of use.\n",
    "  - [open source](https://github.com/igraph) and free\n",
    "  - the source code of igraph packages is written in C\n",
    "  - can be programmed in \n",
    "    [Python](https://igraph.org/python),\n",
    "    [R](https://igraph.org/r),\n",
    "    [Mathematica](http://szhorvat.net/mathematica/IGraphM),\n",
    "    and [C/C++](https://igraph.org/c)\n",
    "  - python-igraph documentation:\n",
    "    [User's Manual with Tutorial](https://igraph.org/python/doc/tutorial/),\n",
    "    [API reference](https://igraph.org/python/doc/api/index.html)\n",
    "    - supports inline plots within a Jupyter notebook via both the Cairo and matplotlib backend\n",
    "    - you can generate graph from edges stored in a _pandas.DataFrame_\n",
    "  - capable of handling large networks efficiently\n",
    "  - interactive and non-interactive usage are both supported"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [python-graph](https://github.com/Shoobx/python-graph) -- A library for working with graphs in Python\n",
    "  - no documentation (?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to import appropriate libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Imports for graphs (NetworkX and matplotlib)\n",
    "import networkx as nx\n",
    "#import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create crown DAG (Directed Acyclic Graph), know as $S_3^0$ graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Create a crown DAG known as $S_3^0$ graph\n",
    "DG=nx.DiGraph()\n",
    "DG.add_nodes_from([1,2,3,4,'u','v'])\n",
    "DG.add_edges_from([(1,2),(3,4),(1,'v'),(3,'v'),('u',2),('u',4)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw the $S_3^0$ graph using \\[automatic\\] shell layout,  \n",
    "using `draw_shell` from networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Draw the $S_3^0$ graph using [automatic] shell layout\n",
    "nx.draw_shell(DG,with_labels=True,node_size=500,width=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See https://networkx.github.io/documentation/stable/auto_examples/index.html for more examples"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}