Lab 3a - Undirected graphs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span>\n",
    "<b>Author:</b> <a href=\"http://about.giuliorossetti.net\">Giulio Rossetti</a><br/>\n",
    "<b>Python version:</b>  >=3.7<br/>\n",
    "</span>\n",
    "\n",
    "<a id='top'></a>\n",
    "\n",
    "``Networkx`` is a python library designed to provide support to analysis of complex networks.\n",
    "\n",
    "In this notebook are introduced some of the main features of the library and an overview of its functionalities.\n",
    "\n",
    "**Note:** this notebook is purposely not 100% comprehensive, it only discusses the basic things you need to get started. <br/> A complete documentation (and tutorial) is available on the project [website](https://networkx.github.io/documentation/latest/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "To install ``networkx`` use the following command:\n",
    "\n",
    "    pip install networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the library\n",
    "As a first step just import the ``networkx`` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import powerlaw\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our example we will not only analyse graphs but also visualise them: for this reason we have to import also ``matplotlib``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design our first graph\n",
    "\n",
    "``networkx`` provides support for several graph models. \n",
    "\n",
    "Among them:\n",
    "- undirected graphs, available through the ``Graph`` class\n",
    "- directed graphs, available through the ``DiGraph`` class\n",
    "\n",
    "In this brief tutorial we will focus only on undirected graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can build a graph adding nodes as well as edges as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = nx.Graph()\n",
    "\n",
    "g.add_node(\"a\")\n",
    "g.add_edge(\"a\", \"b\")\n",
    "g.add_edge(\"a\", \"c\")\n",
    "g.add_edge(\"b\", \"c\")\n",
    "\n",
    "nx.draw_networkx(g ,with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nodes and edges can also be easly removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H4FBTbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g.remove_node(\"a\")\n",
    "g.remove_edge(\"b\", \"c\")\n",
    "\n",
    "#re-draw the graph\n",
    "nx.draw_networkx(g, with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a graph from file\n",
    "``networkx`` natively supports several network file formats.\n",
    "\n",
    "Among them one the most frequently used in online repository is the *edgelist* one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An edge list is a text file (usually saved as .csv) in which each line identifies an edge. <br/>\n",
    "For instance, the triangle defined before can be described as:\n",
    "\n",
    "    a,b\n",
    "    b,c\n",
    "    c,a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read edgelist file just write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.read_edgelist(\"data/network.csv\", delimiter=\",\", nodetype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly a graph can be written to file using ``nx.write_edgelist(g, filename)``.\n",
    "\n",
    "For all the I/O methods refer to the [official documentation](https://networkx.github.io/documentation/latest/reference/readwrite/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing nodes and edges\n",
    "Given a ``Graph`` object is it possible to iterate over its nodes with a simple ``for`` loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in g.nodes(data=True):\n",
    "    # do something\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following a similar rationale is it also possible to loop over the edge set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for e in g.edges():\n",
    "    # do something\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All graph entities can be used to store additional attributes (weights, labels...). \n",
    "\n",
    "For furhter details refer to the [official documentation](https://networkx.github.io/documentation/latest/tutorial.html#adding-attributes-to-graphs-nodes-and-edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network base statistics\n",
    "``networkx`` allows to manipulate nodes as well as edges, count them, and extract relevant global features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2566"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.number_of_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8593"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.number_of_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.is_directed()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Degrees and Degree distribution\n",
    "Node degree can be easily obtained as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.degree(1) # degree for node 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly the average degree can be computed with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.697583787996883"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dict(g.degree()).values())/float(len(g))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An easy way to compute, and visualise, the degree distribution is the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = nx.degree_histogram(g)\n",
    "    \n",
    "plt.plot(range(0, len(hist)), hist, \".\")\n",
    "plt.title(\"Degree Distribution\")\n",
    "plt.xlabel(\"Degree\")\n",
    "plt.ylabel(\"#Nodes\")\n",
    "plt.loglog()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more complete one instead is..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dist(G):\n",
    "    M = nx.to_scipy_sparse_matrix(G)\n",
    "    xmin = min([d[1] for d in G.degree()])\n",
    "    indegrees = M.sum(0).A[0]\n",
    "    degree = np.bincount(indegrees)\n",
    "    fit = powerlaw.Fit(np.array(degree)+1, fit_method='KS')#, xmin=xmin, xmax=max(degree)-xmin,discrete=True)\n",
    "    \n",
    "    \n",
    "    fig = plt.figure(figsize=(16, 6)) \n",
    "    \"\"\" Plot Distribution \"\"\"\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.plot(range(len(degree)),degree,'b.')   \n",
    "    plt.loglog()\n",
    "    plt.xlim((min(degree), max(degree)))\n",
    "    plt.xlabel('Degree')\n",
    "    plt.ylabel('P(k)')\n",
    "\n",
    "\n",
    "    \"\"\" Plot CDF \"\"\"\n",
    "    plt.subplot(1, 3, 2)\n",
    "    fit.plot_cdf()\n",
    "    plt.xlabel(\"Degree\")\n",
    "    plt.ylabel('CDF')\n",
    "\n",
    "    \"\"\" Plot CCDF \"\"\"\n",
    "    plt.subplot(1, 3, 3)\n",
    "    fit.plot_ccdf()\n",
    "    plt.ylabel('CCDF')\n",
    "    plt.xlabel('Degree')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating best minimal value for power law fit\n",
      "xmin progress: 96%\r"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dist(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph components\n",
    "``networkx`` allows to select node specific views of the original graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[306, 830, 1599, 273, 1988]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(g.neighbors(0)) # obtain the list of neighobors for node 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ego = nx.ego_graph(g, 0) # ego network of the node 0\n",
    "nx.draw_networkx(ego, with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the same rationale also connected components can be extracted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.number_connected_components(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HufFfCQD4gA0bNpz4dsF169ZpwIABJieCN2FNAABezOVyadmyZRowYIDatWsnp9NJEcAFYzIAAF4qJydHkZGRSkhI0L333qt77rlHAQEBZseCF6IMAIAXWrdunaKiohQYGKj169erX79+ZkeCF2NNAABepKysTHfffbcGDRqkjh07yul0UgRwyZgMAICX+PHHHxUeHq4vv/xSDz/8sBYsWCCrldd0uHSUAQDwAp9++qnGjh2rGjVqaOPGjerVq5fZkeBDqJQA4MGOHz+u+fPna+jQoerRo4ccDgdFAOWOyQAAeKgffvhBYWFhysjI0OOPP665c+eyFkCFoAwAgAf697//rZiYGAUHB2vz5s3q0aOH2ZHgw6iYAOBBSktLNXv2bN1+++3q27evHA4HRQAVjskAAHiI3bt3y263a8uWLXrmmWc0c+ZMWSwWs2PBD1AGAMADrFq1SnFxcapfv76SkpLUtWtXsyPBj7AmAAATFRcXa9q0aQoNDdXAgQPlcDgoAqh0TAYAwCQ7d+6U3W7Xtm3b9Pe//11TpkxhLQBTMBkAABOsXLlSnTp1Un5+vlJSUjR16lSKAExDGQCASlRUVKRJkyYpPDxcw4YNU1ZWljp27Gh2LPg51gQAUEm++eYb2Ww27dy5Uy+//LLGjx/PNAAegckAAFSCN954Q507d9bx48eVlpamCRMmUATgMSgDAFCBCgoKFBsbq7Fjx2r06NFKT09Xhw4dzI4FnII1AQBUkG3btslms2nPnj3617/+pZiYGLMjAafFZAAAyplhGHrttdfUtWtXWSwWpaenUwTg0SgDAFCOjh07pujoaMXFxSkiIkJpaWm69tprzY4FnBVrAgAoJ1u2bJHNZtNPP/2kt956S5GRkWZHAs4LkwEAuESGYegf//iHunfvrurVqyszM5MiAK9CGQCAS5CXl6fw8HBNnjxZsbGxSklJUZs2bcyOBVwQ1gQAcJGysrJks9m0f/9+vfvuu7LZbGZHAi4KkwEAuECGYej555/XjTfeqODg4BOlAPBWlAEAuABHjx7V6NGjNWPGDE2aNEnJyclq2bKl2bGAS8KaAADOU1pamux2u44cOaIPPvhAd9xxh9mRgHLBZAAAzsEwDD399NPq1auX/vKXv8jhcFAE4FMoAwBwFocPH9aIESM0d+5czZgxQ4mJibr66qvNjgWUK9YEAHAGycnJCgsL07Fjx7RmzRoNGzbM7EhAhWAyAAB/4Ha79dhjj6lPnz5q3LixnE4nRQA+jTIAACc5cOCAhg4dqrvuukvz5s3Tpk2b1LRpU7NjARWKNQEA/Gbz5s0KCwtTaWmp/vOf/+jWW281OxJQKZgMAPB7brdbf/3rX9WvXz+1aNFCTqeTIgC/QhkA4Nf27dunwYMHa8mSJbr77ru1YcMGNWrUyOxYQKViTQDAb23YsEGRkZFyu936/PPPdcstt5gdCTAFkwEAfsflcunee+/VgAED1LZtWzmdTooA/BqTAQB+5eeff1ZERIQSEhJ077336p577lFAQIDZsQBTUQYA+I1169YpKipKAQEBWr9+vfr162d2JMAjsCYA4PPKysp0zz33aPDgwerYsaO2bNlCEQBOwmQAgE/78ccfFRERoaSkJD344INauHChrFZeBwEnowwA8Fn/+c9/NGbMGFWvXl0bN25U7969zY4EeCTqMQCfc/z4cS1YsEBDhgxR9+7d5XQ6KQLAWTAZAOBTsrOzFRYWpvT0dD3++OOaO3cuawHgHCgDAHzGmjVrFBMTo1q1aikhIUE33nij2ZEAr0BdBuD1SktLNWfOHI0YMUK9e/eWw+GgCAAXgMkAAK/2/fffy263y+l06plnntHMmTNlsVjMjgV4FcoAAK/1wQcfKC4uTvXq1VNSUpK6du1qdiTAK7EmAOB1iouLNX36dI0ePVoDBgxQVlYWRQC4BEwGAHiVXbt2yWazadu2bXr++ec1depU1gLAJWIyAMBrrFy5Up06dVJ+fr5SUlI0bdo0igBQDigDADxeUVGRJk2apPDwcA0ZMkSZmZnq2LGj2bEAn8GaAIBH+/bbb2Wz2bRjxw69/PLLGj9+PNMAoJwxGQDgsd566y117txZJSUlSk1N1YQJEygCQAWgDADwOIWFhRo3bpyio6N1xx13KCMjQ9ddd53ZsQCfxZoAgEfZtm2bbDabvv/+e7322muKiYlhGgBUMCYDADyCYRj617/+deLzAjIyMhQbG0sRACoBZQCA6Y4dO6YxY8Zo3LhxCg8PV3p6uq699lqzYwF+gzUBAFP973//k81m048//qg333xTUVFRZkcC/A6TAQCmMAxDL7/8srp166Zq1aopIyODIgCYhDIAoNLl5eUpIiJCkyZNUkxMjFJSUnTNNdeYHQvwW6wJAFQqh8Mhm82mffv2aeXKlbLb7WZHAvwekwEAlcIwDP39739Xjx49VLt2bWVlZVEEAA9BGQBQ4Y4eParQ0FBNnz5dEydOVHJyslq2bGl2LAC/YU0AoEKlp6fLbrfr8OHDWrVqlUaNGmV2JAB/wGQAQIUwDEPPPPOMevbsqQYNGsjhcFAEAA9FGQBQ7g4fPqzbb79dc+bM0fTp05WYmKirr77a7FgAzoA1AYBy9eWXXyosLEz5+fn697//reHDh5sdCcA5MBkAUC7cbrcef/xx9enTR40aNZLD4aAIAF6CMgDgkh08eFDDhg3TggULNHfuXG3atElXXXWV2bEAnCfWBAAuyebNmxUeHq7i4mJ9+umnuu2228yOBOACMRkAcFHcbrceeugh9e/fX82bN5fT6aQIAF6KMgDggu3fv1+DBw/W4sWLtXDhQm3YsEGNGzc2OxaAi8SaAMAFiY+PV0REhNxutz7//HPdcsstZkcCcImYDAA4Ly6XS/fdd58GDBigtm3byul0UgQAH8FkAMA5/fzzz4qKilJ8fLyWLVumxYsXKyAgwOxYAMoJZQDAWf33v/9VVFSUrFar1q9fr/79+5sdCUA5Y00A4LTKysq0ePFiDRo0SNdff72cTidFAPBRTAYA/MmPP/6oiIgIJSUl6cEHH9TChQtltfLaAfBVlAEAp/jss88UHR2t6tWra+PGjerdu7fZkQBUMKo+AEnS8ePHddddd+m2225Tt27d5HQ6KQKAn2AyAEDZ2dkKCwtTWlqaHnvsMd15552sBQA/QhkA/NyaNWsUExOjWrVqafPmzbrxxhvNjgSgklH9AT9VWlqquXPnasSIEerdu7ccDgdFAPBTTAYAP/T9998rLCxMDodDTz/9tGbNmiWLxWJ2LAAmoQwAfubDDz/UuHHjVLduXSUlJalr165mRwJgMtYEgJ8oKSnRjBkzNGrUKN18881yOBwUAQCSmAwAfmHXrl2y2+366quv9Pzzz2vq1KmsBQCcwGQA8HHvvvuuOnXqpNzcXH355ZeaNm0aRQDAKSgDgI8qKirS5MmTFRYWpttuu01ZWVnq1KmT2bEAeCDWBIAP+vbbb2Wz2fTtt9/qH//4hyZMmMA0AMAZMRkAfMxbb72lzp07q6SkRGlpaZo4cSJFAMBZUQYAH1FYWKi4uDhFR0dr5MiRysjI0HXXXWd2LABegDUB4AO+/vpr2Ww27d69W6+99ppiYmKYBgA4b0wGAC+3fPlydenSRYZhKD09XbGxsRQBABeEMgB4qWPHjmns2LGKjY098Y2D7dq1MzsWAC/EmgDwQlu3bpXNZtPevXv1xhtvKDo62uxIALwYkwHAixiGoX/+85/q1q2bqlSpooyMDIoAgEtGGQC8RF5eniIiIjRx4kSNGTNGqampuuaaa8yOBcAHsCYAvIDD4ZDNZtMvv/yid955R2FhYWZHAuBDmAwAHswwDL3wwgvq0aOHatWqpaysLIoAgHJHGQA8VG5urmw2m6ZNm6YJEyYoOTlZrVq1MjsWAB/EmgDwQOnp6bLb7Tp8+LBWrVqlUaNGmR0JgA9jMgB4EMMw9Oyzz6pnz55q0KCBsrKyKAIAKhxlAPAQhw8f1siRIzV79mxNmzZNiYmJat68udmxAPgB1gSAB0hJSZHdbldeXp5Wr16tESNGmB0JgB9hMgCYyO1264knnlDv3r115ZVXyul0UgQAVDrKAGCSgwcPavjw4Zo/f77mzJmjhIQEXXXVVWbHAuCHWBMAJkhMTFR4eLiKior0ySefaMiQIWZHAuDHmAwAlcjtduvhhx9Wv3791KxZMzmdTooAANNRBoBKsn//ft1666265557dNdddyk+Pl6NGzc2OxYAsCYAKsPGjRsVERGhsrIyrV27VgMHDjQ7EgCcwGQAqEAul0v333+/br75ZrVp00ZbtmyhCADwOEwGgAryyy+/KDIyUvHx8Vq6dKmWLFmigIAAs2MBwJ9QBoAK8MUXXygyMlJWq1VffPGFbrrpJrMjAcAZsSYAylFZWZmWLFmigQMH6rrrrpPT6aQIAPB4TAaAcvLTTz8pIiJCiYmJeuCBB7Ro0SJZrfRtAJ6PMgCUg7Vr1yo6OlpVq1ZVfHy8+vTpY3YkADhvvGwBLsHx48e1cOFC3XrrrerataucTidFAIDXYTIAXKTs7GyFh4crNTVVjz76qObNm8daAIBXogwAF+Hjjz9WTEyMatasqYSEBIWEhJgdCQAuGi9jgAtQWlqqO++8U8OHD1fPnj3ldDopAgC8HpMB4Dx9//33CgsLk8Ph0FNPPaXZs2fLYrGYHQsALhllADgPH330kWJjY1W3bl0lJiaqW7duZkcCgHLDmgA4i5KSEs2cOVN33HGHbr75ZjkcDooAAJ/DZAA4g++++052u11bt27Vc889p2nTprEWAOCTKAPAabz33nsaP368GjZsqOTkZHXu3NnsSABQYVgTACcpLi7WlClTZLfbdeuttyorK4siAMDnMRkAfrNjxw7ZbDZ98803eumllzRx4kTWAgD8ApMBQNKKFSvUqVMnFRUVKTU1VZMmTaIIAPAblAH4tcLCQo0fP15RUVEaOXKkMjMzdf3115sdCwAqFWsC+K2vv/5aNptNu3fv1quvvqrY2FimAQD8EpMB+KXly5era9eucrvdSk9P17hx4ygCAPwWZQB+5dixYxo7dqxiY2Nlt9uVnp6udu3amR0LAEzFmgB+Y+vWrbLZbMrOztYbb7yh6OhosyMBgEdgMgCfZxiGXnnlFXXr1k1VqlRRZmYmRQAATkIZgE/Lz89XZGSkJkyYoOjoaKWmpuqaa64xOxYAeBTWBPBZTqdTNptNP//8s95++22Fh4ebHQkAPBKTAfgcwzD04osvqkePHqpZs6aysrIoAgBwFpQB+JTc3FzZ7XZNnTpV48eP15dffqlWrVqZHQsAPBprAviMjIwM2e12HTx4UO+//75Gjx5tdiQA8ApMBuD1DMPQs88+q5CQENWrV08Oh4MiAAAXgDIAr3bkyBHdcccdmj17tqZNm6bExEQ1b97c7FgA4FVYE8Brpaamym63Kzc3V6tXr9aIESPMjgQAXonJALyO2+3Wk08+qV69eun//u//5HQ6KQIAcAkoA/Aqhw4d0vDhwzVv3jzNnj1bCQkJuuqqq8yOBQBejTUBvEZSUpLCwsJUVFSkTz75REOGDDE7EgD4BCYD8Hhut1uPPPKI+vbtq2bNmsnpdFIEAKAcUQbg0fbv36/bbrtNixYt0oIFCxQfH6/GjRubHQsAfAprAnisTZs2KTw8XGVlZVq7dq0GDRpkdiQA8ElMBuBxXC6XHnjgAd10001q06aNnE4nRQAAKhCTAXiUX375RVFRUdqwYYOWLFmipUuXKiAgwOxYAODTKAPwGOvXr1dkZKQk6YsvvtBNN91kciIA8A+sCWC6srIyLV26VLfccos6dOigLVu2UAQAoBIxGYCpcnJyFB4ersTERN1///1atGgRawEAqGSUAZhm7dq1io6OVtWqVbVhwwb17dvX7EgA4JdYE6DSlZWVadGiRbr11lvVpUsXOZ1OigAAmIjJACrV3r17FR4erpSUFD3yyCOaP3++rFY6KQCYiTKASvPJJ59o7NixqlmzphISEhQSEmJ2JACAWBOgEpSWlmrevHkaNmyYevbsKYfDQREAAA/CZAAVas+ePQoLC1NmZqaefPJJzZkzRxaLxexYAICTUAZQYVavXq3Y2FgFBwcrMTFR3bt3NzsSAOA0WBOg3JWUlGjWrFkaOXKk+vfvL4fDQREAAA/GZADl6rvvvpPdbtfWrVv1t7/9TdOnT2ctAAAejjKAcvP+++9r/PjxatCggZKTk9W5c2ezIwEAzgNrAlyy4uJiTZ06VTabTYMHD1ZWVhZFAAC8CJMBXJIdO3bIZrPpm2++0YsvvqhJkyaxFgAAL8NkABft7bffVufOnVVUVKTU1FRNnjyZIgAAXogygAtWWFioCRMmKDIyUiNGjFBGRoauv/56s2MBAC4SawJckO3bt8tms+m7777TK6+8onHjxjENAAAvx2QA5+31119Xly5d5HK5lJaWpri4OIoAAPgAygDOqaCgQDExMYqJiZHNZlN6errat29vdiwAQDlhTYCz+uqrrxQaGqrs7Gy9/vrrGjNmjNmRAADljMkATsswDL3yyivq2rWrAgMDlZGRQREAAB9FGcCf5OfnKyoqShMmTFB0dLTS0tLUtm1bs2MBACoIawKcwul0ym63KycnRytWrFBERITZkQAAFYzJACT9uhZ48cUX1aNHDwUFBSkzM5MiAAB+gjIA5ebmKiwsTFOnTlVcXJy+/PJLtW7d2uxYAIBKwprAz2VmZspms+ngwYN67733FBoaanYkAEAlYzLgpwzD0HPPPaeQkBDVrVtXWVlZFAEA8FOUAT905MgRjRo1SjNnztSUKVOUlJSkFi1amB0LAGAS1gR+JjU1VWFhYTp69Kg++ugj3X777WZHAgCYjMmAnzAMQ08++aR69eqlyy+/XA6HgyIAAJBEGfALhw4d0vDhwzVv3jzNmjVLCQkJatasmdmxAAAegjWBj0tKSlJ4eLgKCgr08ccfa+jQoWZHAgB4GCYDPsrtduuRRx5R37591bRpUzmdTooAAOC0KAM+6MCBAxoyZIgWLVqkBQsWKD4+Xk2aNDE7FgDAQ7Em8DEJCQkKDw/X8ePHtXbtWg0aNMjsSAAAD8dkwEe4XC49+OCD6t+/v1q1aiWn00kRAACcFyYDPmDfvn2KjIzUhg0btGTJEi1ZskSBgfyrBQCcH54xvNz69esVGRkpSfrvf/+rm2++2eREAABvw5rAS7lcLi1btky33HKL2rdvL6fTSREAAFwUJgNeKCcnR5GRkUpISNB9992nu+++WwEBAWbHAgB4KcqAl/n8888VHR2tKlWqaMOGDerbt6/ZkQAAXo41gZcoKyvTokWLNHjwYHXq1ElOp5MiAAAoF0wGvMDevXsVHh6ulJQUPfzww1qwYIGsVnocAKB8UAY83KeffqoxY8YoKChImzZtUs+ePc2OBADwMby89FDHjx/X/PnzNXToUIWEhMjpdFIEAAAVgsmAB/rhhx9kt9uVmZmpJ598UnPmzJHFYjE7FgDAR1EGPMzq1asVGxur4OBgJSYmqnv37mZHAgD4ONYEHqK0tFSzZ8/WyJEj1a9fPzkcDooAAKBSMBnwALt375bdbteWLVv07LPPasaMGawFAACVhjJgslWrVikuLk4NGjRQcnKyunTpYnYkAICfYU1gkuLiYk2bNk2hoaEaNGiQsrKyKAIAAFMwGTDBzp07ZbPZtH37dr3wwguaPHkyawEAgGmYDFSyd955R506dVJBQYFSUlI0ZcoUigAAwFSUgUpSVFSkiRMnKiIiQsOHD1dmZqZuuOEGs2MBAMCaoDJ88803Cg0N1a5du/TKK69o3LhxTAMAAB6DyUAFe+ONN9S5c2eVlZUpPT1dcXFxFAEAgEehDFSQgoICxcbGauzYsQoNDVVGRobat29vdiwAAP6ENUEF2LZtm2w2m/bs2aPly5dr7NixZkcCAOCMmAyUI8Mw9Oqrr6pr166yWq1KT0+nCAAAPB5loJzk5+crOjpa48ePV2RkpFJTU3XttdeaHQsAgHNiTVAOtmzZIpvNppycHK1YsUIRERFmRwIA4LwxGbgEhmHopZdeUvfu3VWjRg1lZmZSBAAAXocycJHy8vIUFhamKVOmaNy4cUpJSVHr1q3NjgUAwAVjTXARMjMzZbfbdeDAAb377ruy2WxmRwIA4KIxGbgAhmHoueeeU0hIiOrUqaOsrCyKAADA61EGztPRo0c1evRozZw5U5MnT1ZSUpJatGhhdiwAAC4Za4LzkJaWJrvdrqNHj+rDDz/UyJEjzY4EAEC5YTJwFoZh6KmnnlLPnj3VsGFDORwOigAAwOdQBs7g8OHDGjFihO68807NmjVLmzdvVrNmzcyOBQBAuWNNcBrJyckKCwtTQUGB1qxZo2HDhpkdCQCACuNTk4GCkjJty8mVI/uItuXkqqCk7IJ+3u1267HHHlOfPn3UpEkTOZ1OigAAwOd5/WRg5758rUjNVvy3+5V9uFDGSfdZJDWtF6T+bRoqsntTtbq81hnPc+DAAY0dO1afffaZFi5cqPvvv19VqlSp8PwAAJjNYhiGca6D8vLyFBwcrNzcXNWuXbsycp3T3sOFuvujrdq866ACrBa53Gd+GL/f37tlAz00soOa1As65f6EhASFh4ertLRUb775pgYPHlzR8QEAqHDn+/ztlWuClenZGvD0JiXvPiRJZy0CJ9+fvPuQBjy9SSvTs3+93eXSgw8+qP79+6tly5ZyOp0UAQCA3/G6NcHz8Tv1xLodF/WzLrchl9vQwg+3as8vh7T+bwu0fv16LV68WEuXLlVgoNf9dQAAcMm86tlvZXr2RReBP3opOUfHi4O1bt06DRgwoFzOCQCAN/KaMrD3cKGWrdmmkp93qGDrehVnb1VZ7j5Za9RWtSvbqE6faFWp10iSZBhuFWzdoMIdySrdt1vu4nwFBl+uoLZ9FNz9DlkCq0qGocv6jlObTiEmPzIAAMzlNdcM3P3RVpW5DeWlrFLht8mqftX1qjtgoi67fpCK936ln/81S6UH9kiSjOMlOvSfZ+QqzFWtjreq7s0TVPX/Wis38W3te2+ZDMOQLBaVGb+eFwAAf+YVk4Gd+/K1eddBSVKtriPVYPh8WQL+/9v+arbtrZxXpysvZZUaDJsnS0CgLo96XNUbtz1xTK0bBisw+HLlJq5Q8Q9bVKPZDXK5DW3edVC79uerZcMzv+0QAABf5hWTgRWp2QqwWiRJ1Ru3PaUISFKVeo1UtUFTHT+4V5JkCahyShH4XVDrGyXpxHHSr287fCslu6KiAwDg8byiDMR/u/+sbx80DEOuwqOyBp39MxBcBUckSQEnHedyG4rfsb98ggIA4IU8vgwcKylT9uHCsx5TsG2jXPmHVPOa3mc9Li/1A1mqBalG886n3J59qPCCP7oYAABf4fFl4IdDBTrbRwodP7RXh//7oqo1ukY1O9x8xuNyk99T8R6n6vaNkbX6ZafcZ0jac6igfAIDAOBlPL4MlJa5z3if69gR7X//Plmr1VSD2xfJYg047XEF2xN0NOFNXXbdQNXqdNsF/x4AAHyZx7+boGrg6fuKu7hA+95bJndxgS6PelSBteqf9rii7x06+MlTqtGii+oNnnbBvwcAAF/n8c+AzerXlOUPtxllpdq/6n6VHflJDUOXqmqDpqf92ZKcb3Xgw7+q2hWt1OD2hWecHFh++z0AAPgjjy8DNasFqulJ3zJouF06sPpRleR8o7/cvlDVGv35LYTSr28f3P/+fQoMbqi/hC6TtUq1M/6OpvWDVLOaxw9JAACoEF7xDNi/TUO9mfqDXG5DRza8qqJdqarRsptcRcd07Kv4U469rH1/uUsKte+9pXIXH1Pt7neoaFf6KcdUqXvFiRIRYLWof+uGlfZYAADwNF5RBiK7N9XyL/dIkkr37ZYkFe1KU9GutD8de1n7/nIX5cuVd0CSdHTj8j8dU7P9zSfKgMttKKrH6dcMAAD4A68oA60ur6XeLRsoefchXRH5yDmPD6xzua5a+Mk5jwuwWhTSvD4fRQwA8Gsef83A7x4a2UGB1j9eSnhpAq0WPTSyQ7meEwAAb+M1ZaBJvSDdN7xduZ7z/uHt1OSkixMBAPBHXlMGJCmsa1PNG9i6XM41f2Ab2btyrQAAAF5xzcDJpvdvpQaXVdOyNdtU5jbO+gVGfxRgtSjQatH9w9tRBAAA+I1XTQZ+F9a1qb6Y01chzX/91MGAc1xL8Pv9Ic3r64s5fSkCAACcxOsmA79rUi9Ib8Z11859+VqRmq34HfuVfajwlC81sujXDxTq37qhono05V0DAACchsUwjHPO2fPy8hQcHKzc3FzVrl27MnJdlIKSMu05VKDSMreqBlrVrH5NPlkQAOC3zvf526eeKWtWC1S7K4PNjgEAgFfxymsGAABA+aEMAADg5ygDAAD4OcoAAAB+jjIAAICfowwAAODnKAMAAPg5ygAAAH6OMgAAgJ+jDAAA4OcoAwAA+DnKAAAAfo4yAACAn6MMAADg5ygDAAD4OcoAAAB+LvB8DjIMQ5KUl5dXoWEAAED5+f15+/fn8TM5rzKQn58vSWrSpMklxgIAAJUtPz9fwcHBZ7zfYpyrLkhyu93KyclRrVq1ZLFYyjUgAACoGIZhKD8/X1deeaWs1jNfGXBeZQAAAPguLiAEAMDPUQYAAPBzlAEAAPwcZQAAAD9HGQAAwM9RBgAA8HOUAQAA/Nz/A+gXH4reFvrdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comps = list(nx.connected_components(g)) # get a list of connected components (for decreasing size)\n",
    "comp_1 = nx.subgraph(g, comps[1]) # build a subgraph on the second component\n",
    "nx.draw_networkx(comp_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paths and Diameter\n",
    "Shortest paths can be extracted as well using the following syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 306, 30]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.shortest_path(g, source=0, target=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.shortest_path_length(g, source=0, target=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, the network diameter can be computed as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.diameter(g.subgraph(comps[0])) # we compute the diameter on the giant component"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Triangles, density and clustering\n",
    "Other indexes that can be computed using the library are "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002611143776996835"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.density(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.triangles(g)[0] # count the triangles each node is involved in (and access the value of node 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.clustering(g)[0] # compute the local clustering coefficient for all nodes (and access the value for node 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20063633264589634"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.average_clustering(g) # compute the global clustering coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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