Visualising Graph Data with python-igraph

Visualise_Graph_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising and Analysing the Labelled CiteSeer Network Dataset\n",
    "## Build the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from igraph import *\n",
    "\n",
    "n_vertices = 3264\n",
    "\n",
    "# Create graph\n",
    "g = Graph()\n",
    "\n",
    "# Add vertices\n",
    "g.add_vertices(n_vertices)\n",
    "\n",
    "edges = []\n",
    "weights = []\n",
    "\n",
    "with open(\"citeseer.edges\", \"r\") as edges_file:\n",
    "    line = edges_file.readline()\n",
    "    \n",
    "    while line != \"\":\n",
    "        \n",
    "        strings = line.rstrip().split(\",\")\n",
    "        \n",
    "        # Add edge to edge list\n",
    "        edges.append(((int(strings[0])-1), (int(strings[1])-1)))\n",
    "        \n",
    "        # Add weight to weight list\n",
    "        weights.append(float(strings[2]))\n",
    "        \n",
    "        \n",
    "        line = edges_file.readline()\n",
    "\n",
    "# Add edges to the graph\n",
    "g.add_edges(edges)\n",
    "\n",
    "# Add weights to edges in the graph\n",
    "g.es['weight'] = weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_fig_name = \"graph.eps\"\n",
    "\n",
    "visual_style = {}\n",
    "\n",
    "# Define colors used for outdegree visualization\n",
    "colours = ['#fecc5c', '#a31a1c']\n",
    "\n",
    "# Set bbox and margin\n",
    "visual_style[\"bbox\"] = (3000,3000)\n",
    "visual_style[\"margin\"] = 17\n",
    "\n",
    "# Set vertex colours\n",
    "visual_style[\"vertex_color\"] = 'grey'\n",
    "\n",
    "# Set vertex size\n",
    "visual_style[\"vertex_size\"] = 20\n",
    "\n",
    "# Set vertex lable size\n",
    "visual_style[\"vertex_label_size\"] = 8\n",
    "\n",
    "# Don't curve the edges\n",
    "visual_style[\"edge_curved\"] = False\n",
    "\n",
    "# Set the layout\n",
    "my_layout = g.layout_fruchterman_reingold()\n",
    "visual_style[\"layout\"] = my_layout\n",
    "\n",
    "# Plot the graph\n",
    "plot(g, out_fig_name, **visual_style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise the Graph according to Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = 6\n",
    "\n",
    "bins = [[] for x in range(n_classes)]\n",
    "\n",
    "with open(\"citeseer.node_labels\", \"r\") as labels_file:\n",
    "    line = labels_file.readline()\n",
    "    \n",
    "    while line != \"\":\n",
    "        \n",
    "        strings = line.rstrip().split(\",\")\n",
    "        \n",
    "        vertex_id = int(strings[0])-1\n",
    "        bin_id = int(strings[1])-1\n",
    "        bins[bin_id].append(vertex_id)\n",
    "        \n",
    "        line = labels_file.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_colours = []\n",
    "\n",
    "for i in range(n_vertices):\n",
    "    if i in bins[0]:\n",
    "        node_colours.append(\"green\")\n",
    "    elif i in bins[1]:\n",
    "        node_colours.append(\"yellow\")\n",
    "    elif i in bins[2]:\n",
    "        node_colours.append(\"red\")\n",
    "    elif i in bins[3]:\n",
    "        node_colours.append(\"blue\")\n",
    "    elif i in bins[4]:\n",
    "        node_colours.append(\"orange\")\n",
    "    elif i in bins[5]:\n",
    "        node_colours.append(\"pink\")\n",
    "    else:\n",
    "        node_colours.append(\"grey\")\n",
    "\n",
    "out_fig_name = \"labelled_graph.eps\"\n",
    "\n",
    "g.vs[\"color\"] = node_colours\n",
    "\n",
    "visual_style = {}\n",
    "\n",
    "# Define colors used for outdegree visualization\n",
    "colours = ['#fecc5c', '#a31a1c']\n",
    "\n",
    "# Set bbox and margin\n",
    "visual_style[\"bbox\"] = (3000,3000)\n",
    "visual_style[\"margin\"] = 17\n",
    "\n",
    "# Set vertex size\n",
    "visual_style[\"vertex_size\"] = 20\n",
    "\n",
    "# Set vertex lable size\n",
    "visual_style[\"vertex_label_size\"] = 8\n",
    "\n",
    "# Don't curve the edges\n",
    "visual_style[\"edge_curved\"] = False\n",
    "\n",
    "# Set the layout\n",
    "visual_style[\"layout\"] = my_layout\n",
    "\n",
    "# Plot the graph\n",
    "plot(g, out_fig_name, **visual_style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of vertices: 3264\n",
      "Number of edges: 4536\n",
      "Density of the graph: 0.000851796434172811\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of vertices:\", g.vcount())\n",
    "print(\"Number of edges:\", g.ecount())\n",
    "print(\"Density of the graph:\", 2*g.ecount()/(g.vcount()*(g.vcount()-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average degree: 2.7794117647058822\n",
      "Maximum degree: 99\n",
      "Vertex ID with the maximum degree: 2906\n"
     ]
    }
   ],
   "source": [
    "degrees = []\n",
    "total = 0\n",
    "\n",
    "for n in range(n_vertices):\n",
    "    neighbours = g.neighbors(n, mode='ALL')\n",
    "    total += len(neighbours)\n",
    "    degrees.append(len(neighbours))\n",
    "    \n",
    "print(\"Average degree:\", total/n_vertices)\n",
    "print(\"Maximum degree:\", max(degrees))\n",
    "print(\"Vertex ID with the maximum degree:\", degrees.index(max(degrees)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree having the maximum number of vertices: 1\n",
      "Number of vertices having the most abundant degree: 1321\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.rcParams.update({'font.size': 25})\n",
    "\n",
    "x = [x for x in range(max(degrees)+1)]\n",
    "degree_counts = [0 for x in range(100)]\n",
    "\n",
    "for i in degrees:\n",
    "    degree_counts[i] += 1\n",
    "\n",
    "print(\"Degree having the maximum number of vertices:\", degree_counts.index(max(degree_counts)))\n",
    "print(\"Number of vertices having the most abundant degree:\", max(degree_counts))\n",
    "\n",
    "plt.figure(figsize=(40,10))\n",
    "plt.plot(x, degree_counts, linewidth=3.0)\n",
    "plt.ylabel('Number of vertices having the given degree')\n",
    "plt.xlabel('Degree')\n",
    "plt.title('Degree Distribution of Vertices in the CiteSeer Graph')\n",
    "\n",
    "plt.xlim(0,100)\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.grid(True)\n",
    "plt.savefig('degree_distribution.png', bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of triangles: 1.0716911764705883\n",
      "Maximum number of triangles: 85\n",
      "Vertex ID with the maximum number of triangles: 2906\n"
     ]
    }
   ],
   "source": [
    "# Source: https://stackoverflow.com/questions/34219481/python-igraph-finding-number-of-triangles-for-each-vertex\n",
    "\n",
    "cliques = g.cliques(min=3, max=3)\n",
    "triangle_count = [0] * g.vcount()\n",
    "for i, j, k in cliques:\n",
    "    triangle_count[i] += 1\n",
    "    triangle_count[j] += 1\n",
    "    triangle_count[k] += 1\n",
    "\n",
    "print(\"Average number of triangles:\", sum(triangle_count)/g.vcount())\n",
    "print(\"Maximum number of triangles:\", max(triangle_count))\n",
    "print(\"Vertex ID with the maximum number of triangles:\", triangle_count.index(max(triangle_count)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diameter of the graph: 28\n"
     ]
    }
   ],
   "source": [
    "print(\"Diameter of the graph:\", g.diameter())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assortativity of the graph: 0.04806382149471062\n"
     ]
    }
   ],
   "source": [
    "# Source: https://snipplr.com/view/9914\n",
    "\n",
    "def assortativity(graph, degrees=None):\n",
    "    if degrees is None: degrees = graph.degree()\n",
    "    degrees_sq = [deg**2 for deg in degrees]\n",
    " \n",
    "    m = float(graph.ecount())\n",
    "    num1, num2, den1 = 0, 0, 0\n",
    "    for source, target in graph.get_edgelist():\n",
    "        num1 += degrees[source] * degrees[target]\n",
    "        num2 += degrees[source] + degrees[target]\n",
    "        den1 += degrees_sq[source] + degrees_sq[target]\n",
    " \n",
    "    num1 /= m\n",
    "    den1 /= 2*m\n",
    "    num2 = (num2 / (2*m)) ** 2\n",
    " \n",
    "    return (num1 - num2) / (den1 - num2)\n",
    "\n",
    "print(\"Assortativity of the graph:\", assortativity(g))"
   ]
  }
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