day_3/case_study_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip3 install networkx\n",
    "#!pip3 install matplotlib\n",
    "#!pip3 install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This group exercise is designed to develop an understanding of basic network measures and to start participants thinking about interesting research questions that can be enabled by network science.\n",
    "\n",
    "<ol>\n",
    "    <li>Divide yourselves into groups of four by counting off in order around the room.</li>\n",
    "    <li>For 10 minutes, explore the <a href=\"https://icon.colorado.edu/#!/networks\">Index of Complex Networks (ICON)</a> database and identify a network your group might like to investigate further. (If someone in your group has a network ready, that you'd all like to analyze feel free to work on this network instead.)</li>\n",
    "    <li>Write code to import this network into Python. Play with the <a href=\"https://networkx.github.io/documentation/stable/reference/algorithms/index.html\">built-in functionality</a> of `networkx`. (See the code below for help with this step.)</li>\n",
    "    <li>For 15 minutes, identify a possible research question using this data. Evaluate the strengths and weaknesses of this data.</li>\n",
    "    <li>Outline a research design that could be used to address the weaknesses of the data you collected (e.g. think about possible data sets you could combine with this network), or otherwise improve your ability to answer the research question.</li>\n",
    "</ol>\n",
    "\n",
    "There is only one requirement: the group member with the least amount of experience coding should be responsible for typing the code into a computer. After 40 minutes you should be prepared to give a 3 minute presentation of your work. Remember that these daily exercises are for you to get to know each other better, are not expected to be fully-fleshed out research project, and a way for you to explore research areas that may be new to you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing ICON data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visit the ICON website (<a href=\"https://icon.colorado.edu/#!/networks\">link</a>). You can search the index using the checkboxes under the tabs \"network domain,\" \"subdomain,\" \"graph properties,\" and \"size\". You can also type in keywords related to the network you would like to find. Here is a screenshot:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"karate.png\" width=\"500px\" align=\"left\"></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To download a network, click the small yellow downward arrow and follow the link listed under \"source\". Importing this data into Python using `networkx` will depend on the file type of the network you download. (Check out the <a href=\"https://networkx.github.io/documentation/stable/reference/readwrite/index.html\">package's documentation</a> for how to import networks from different file types.) \n",
    "\n",
    "Here's what it looks like to import the [Zachary Karate Club](https://en.wikipedia.org/wiki/Zachary%27s_karate_club) from the edglist provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('karate_edges_77.txt', 'rb') as file: \n",
    "    karate_club = nx.read_edgelist(file) # Read in the edges\n",
    "\n",
    "groups = {}\n",
    "with open('karate_groups.txt', 'r') as file:\n",
    "    for line in file:\n",
    "        [node, group] = re.split(r'\\t+', line.strip())\n",
    "        groups[node] = int(group)\n",
    "\n",
    "nx.set_node_attributes(karate_club, name = 'group', values = groups) # Add attributes to the nodes (e.g. group membership)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to `networkx`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For very small networks, it can be helpful to visualize the nodes and edges. Below we have colored the nodes with respect to their group within the karate club."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "position = nx.spring_layout(karate_club)\n",
    "nx.draw_networkx_labels(karate_club, pos = position) \n",
    "\n",
    "colors = [] # Color the nodes acording to their group\n",
    "for attr in nx.get_node_attributes(karate_club, 'group').values():\n",
    "    if attr == 1: colors.append('blue') \n",
    "    else: colors.append('green')\n",
    "        \n",
    "nx.draw(karate_club, position, node_color = colors) # Visualize the graph "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A natural question you might like to ask about a network, is what are the most \"important\" nodes? There are many definitions of network [importance or centrality](http://tuvalu.santafe.edu/~aaronc/courses/5352/csci5352_2017_L2.pdf). Here let's just consider one of the most straightforward measures: degree centrality -- the number of edges that start or end at a given node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('1', 16), ('2', 9), ('3', 10), ('4', 6), ('5', 3), ('6', 4), ('7', 4), ('8', 4), ('9', 5), ('11', 3), ('12', 1), ('13', 2), ('14', 5), ('18', 2), ('20', 3), ('22', 2), ('32', 6), ('31', 4), ('10', 2), ('28', 4), ('29', 3), ('33', 12), ('17', 2), ('34', 16), ('15', 2), ('16', 2), ('19', 2), ('21', 2), ('23', 1), ('24', 5), ('26', 3), ('30', 4), ('25', 3), ('27', 2)]\n"
     ]
    }
   ],
   "source": [
    "print([(n, karate_club.degree(n)) for n in karate_club.nodes()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NetworkX can be used to return a normalized (divided by the maximum possible degree of the network) degree centrality for all nodes in the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1': 0.48484848484848486, '2': 0.2727272727272727, '3': 0.30303030303030304, '4': 0.18181818181818182, '5': 0.09090909090909091, '6': 0.12121212121212122, '7': 0.12121212121212122, '8': 0.12121212121212122, '9': 0.15151515151515152, '11': 0.09090909090909091, '12': 0.030303030303030304, '13': 0.06060606060606061, '14': 0.15151515151515152, '18': 0.06060606060606061, '20': 0.09090909090909091, '22': 0.06060606060606061, '32': 0.18181818181818182, '31': 0.12121212121212122, '10': 0.06060606060606061, '28': 0.12121212121212122, '29': 0.09090909090909091, '33': 0.36363636363636365, '17': 0.06060606060606061, '34': 0.48484848484848486, '15': 0.06060606060606061, '16': 0.06060606060606061, '19': 0.06060606060606061, '21': 0.06060606060606061, '23': 0.030303030303030304, '24': 0.15151515151515152, '26': 0.09090909090909091, '30': 0.12121212121212122, '25': 0.09090909090909091, '27': 0.06060606060606061}\n"
     ]
    }
   ],
   "source": [
    "degrees = nx.degree_centrality(karate_club)\n",
    "print(degrees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From both measures, we can see that nodes 1 and 34 have the highest degree. (These happen to be the two leaders from the two groups within the club.)\n",
    "\n",
    "On large networks, you might want to look at the degree distribution of your network ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enron email data set: http://snap.stanford.edu/data/email-Enron.html.\n",
    "# (You can search \"Email network (Enron corpus)\" in ICON.)\n",
    "with open('email_enron.txt', 'rb') as file: \n",
    "    enron = nx.read_edgelist(file, comments='#') # Read in the edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enron network contains 36692 nodes, and 183831 edges.\n"
     ]
    }
   ],
   "source": [
    "print(\"Enron network contains {0} nodes, and {1} edges.\".format(len(enron.nodes()), len(enron.edges())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average degree: 10.020222391802028, Maximum degree: 1383\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "degree_sequence = list(dict(enron.degree()).values()) \n",
    "print(\"Average degree: {0}, Maximum degree: {1}\".format(np.mean(degree_sequence), max(degree_sequence)))\n",
    "\n",
    "plt.hist(degree_sequence, bins=30) # Plots histogram of degree sequence\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another network feature that you might like to know about your network, is how [assortative or modular](http://tuvalu.santafe.edu/~aaronc/courses/5352/csci5352_2017_L5.pdf) is it. Another way of asking this, is how likely is it for similar nodes to be connected to each other? This similarity can be measured along any number of network attributes. Here we ask, how much more likely are nodes from the same group within the karate club connected to each, than we would expect at random?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assortativity coefficient: 0.7135125993573483\n"
     ]
    }
   ],
   "source": [
    "assort = nx.attribute_assortativity_coefficient(karate_club, 'group') \n",
    "print(\"Assortativity coefficient: {0}\".format(assort))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also add edge attributes, either [all at once using `set_edge_attributes`](https://networkx.github.io/documentation/stable/reference/generated/networkx.classes.function.set_edge_attributes.html?highlight=edge_attributes#networkx.classes.function.set_edge_attributes) (like we did above for `set_node_attributes`), or on an edge by edge basis as shown below. The shortest path between two nodes using that weight can then be calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example borrowed from: https://www.cl.cam.ac.uk/teaching/1314/L109/tutorial.pdf\n",
    "g = nx.Graph()\n",
    "g.add_edge('a', 'b', weight=0.1)\n",
    "g.add_edge('b', 'c', weight=1.5)\n",
    "g.add_edge('a', 'c', weight=1.0)\n",
    "g.add_edge('c', 'd', weight=2.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['b', 'c', 'd']\n",
      "['b', 'a', 'c', 'd']\n"
     ]
    }
   ],
   "source": [
    "print(nx.shortest_path(g, 'b', 'd'))\n",
    "print(nx.shortest_path(g, 'b', 'd', weight='weight'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, one might want to create a function on top of these networks. For example, to measure the average degree of a node's neighbors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example borrowed from: https://www.cl.cam.ac.uk/teaching/1314/L109/tutorial.pdf\n",
    "def avg_neigh_degree(g):\n",
    "    data = {}\n",
    "    for n in g.nodes():\n",
    "        if g.degree(n):\n",
    "            data[n] = float(sum(g.degree(i) for i in g[n]))/g.degree(n) \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 2.5, 'b': 2.5, 'c': 1.6666666666666667, 'd': 3.0}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_neigh_degree(g) # Can you confirm that this is returning the correct results?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, run a similar analysis on the network from ICON (or a network that a group member has ready) you have chosen. Feel free to not be confined by the `networkx` functionality I have shown above; tap into your group's expertise and academic disciplines to identify other network measures you might be interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}