demo_community_classes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Yquetzal/tnetwork/blob/master/demo_community_classes.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamic Community classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "1.  [Initializing a dynamic community structure](#creating)\n",
    "  *   [Using a snapshot representation]\n",
    "  *  [Using an interval graph representation]\n",
    "2. [Accessing properties of communities](#accessing)\n",
    "3. [Duration,frequencies of relations between nodes and communities](#duration)\n",
    "4. [Visualization](#visu)\n",
    "5. [Conversion between snapshots and interval graphs](#conversion)\n",
    "6. [Slicing](#slicing)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If tnerwork library is not installed, you need to install it, for instance using the following command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%capture #avoid printing output\n",
    "#!pip install --upgrade git+https://github.com/Yquetzal/tnetwork.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2\n",
    "import tnetwork as tn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='creating'></a>\n",
    "## Initializing a dynamic community structure\n",
    "### With snapshots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "com_sn = tn.DynCommunitiesSN()\n",
    "com_sn.add_affiliation(\"a\",\"com1\",1)\n",
    "com_sn.add_affiliation({\"b\",\"c\"},\"com2\",[2,3])\n",
    "com_sn.add_affiliation(\"d\",{\"com1\"},[1,3])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With Interval graphs\n",
    "As with dynamic graphs, intervals are closed on the left and open on the right.\n",
    "Periods can be represented in different manners, as shown in the following example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "com_ig = tn.DynCommunitiesIG()\n",
    "com_ig.add_affiliation(\"a\",\"com1\",(1,2))\n",
    "com_ig.add_affiliation({\"b\",\"c\"},\"com2\",tn.Intervals((2,4)))\n",
    "com_ig.add_affiliation(\"d\",{\"com1\"},[(1,2),(3,4)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='accessing'></a>\n",
    "\n",
    "## Accessing properties of communities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check communities\n",
    "We check the sate of communities at a particular time.\n",
    "\n",
    "Static communities can be accessed in two forms\n",
    "\n",
    "   * in the **community** form (key = community ID, value = set of nodes) \n",
    "   * in **affiliation** form (key = a noe, value = set of communities)\n",
    "\n",
    "Example, state of communities at time 3, in *community* and *affiliation* forms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'com2': {'c', 'b'}, 'com1': {'d'}}\n",
      "{'c': {'com2'}, 'b': {'com2'}, 'd': {'com1'}}\n",
      "{'com2': {'b', 'c'}, 'com1': {'d'}}\n",
      "{'c': {'com2'}, 'b': {'com2'}, 'd': {'com1'}}\n"
     ]
    }
   ],
   "source": [
    "print(com_sn.communities(3))\n",
    "print(com_sn.affiliations(3))\n",
    "print(com_ig.communities(3))\n",
    "print(com_ig.affiliations(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same form exist to access dynamic communities.\n",
    "   * communities form: for each community, for each of its nodes, presence time\n",
    "   * affiliation form: for each node, for each of its communities, presence time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'com1': {'a': [1], 'd': [1, 3]}, 'com2': {'c': [2, 3], 'b': [2, 3]}}\n",
      "{'com1': {'a': [1,2[ , 'd': [1,2[ [3,4[ }, 'com2': {'c': [2,4[ , 'b': [2,4[ }}\n",
      "{'a': {'com1': [1]}, 'd': {'com1': [1, 3]}, 'c': {'com2': [2, 3]}, 'b': {'com2': [2, 3]}}\n",
      "{'a': {'com1': [1,2[ }, 'c': {'com2': [2,4[ }, 'b': {'com2': [2,4[ }, 'd': {'com1': [1,2[ [3,4[ }}\n"
     ]
    }
   ],
   "source": [
    "print(com_sn.communities())\n",
    "print(com_ig.communities())\n",
    "print(com_sn.affiliations())\n",
    "print(com_ig.affiliations())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In each snapshot\n",
    "For snapshots representation, it is also possible to obtain communities in each snapshot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SortedDict({1: {'a': {'com1'}, 'd': {'com1'}}, 2: {'c': {'com2'}, 'b': {'com2'}}, 3: {'c': {'com2'}, 'b': {'com2'}, 'd': {'com1'}}})\n"
     ]
    }
   ],
   "source": [
    "print(com_sn.snapshot_affiliations())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='duration'></a>\n",
    "\n",
    "## Duration/frequencies of relations between nodes and communities\n",
    "One can check how long does a node belong to a community, in total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(com_sn.affiliations_durations(\"d\",\"com1\"))\n",
    "print(com_ig.affiliations_durations(\"d\",\"com1\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also check directly the duration of affiliations of each node to each community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('a', 'com1'): 1, ('b', 'com2'): 2, ('c', 'com2'): 2, ('d', 'com1'): 2}\n",
      "{('a', 'com1'): 1, ('b', 'com2'): 2, ('c', 'com2'): 2, ('d', 'com1'): 2}\n"
     ]
    }
   ],
   "source": [
    "print(com_sn.affiliations_durations())\n",
    "print(com_ig.affiliations_durations())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='visu'></a>\n",
    "\n",
    "## Visualization\n",
    "A simple example of visualization. To see more possibilities, see the dedicated section of the documentation\n",
    "\n",
    "Note that it is the same function which is used to plot longitudinial graphs and communities. That is why we need to specify that what we provide corresponds to the `communities` parameter. One can provide both a graph and a dynamic community structure to this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/numpy/core/numeric.py:2327: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return bool(asarray(a1 == a2).all())\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApIAAADCCAYAAAD6mSK4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOsklEQVR4nO3dfYxlZ10H8O9vO7KWfYOUd9pqg1jlJa28CCKhK1hAIQESJUiCVCwIioFKa2h8iyJWQEqJNAGlRpoQ9A9jDRKDBakKbUWovAk2gCvt0gK2hZ3dUhaWffzjzuh03Zmd+/S+nJn5fJKT6T33nHt+2V+fud95zjn3VmstAAAwrm3zLgAAgI1JkAQAoIsgCQBAF0ESAIAugiQAAF0ESQAAugiSAAB0WZj1AauqkjwkycFZHxsAgHXbleSWtsaHjs88SGYUIvfP4bgAAIzn1CRfXu3JeQTJg0ly8803Z/fu3XM4PAAAa1lcXMxpp52WnOAM8jyCZJJk9+7dgiQAwAY2tyD52VsOZOdB3/MNq9mxfSFn3G/HvMuAwdt325258/CReZcBgzat95S5Bcnnv+P6bNt+73kdHjaED124V5iENey77c78xB9dM+8yYEOYxnuKj/+BATPLAmszRmD9pjFeBEkAALoIkgAAdBEkAQDoIkgCANBFkAQAoIsgCQBAF0ESAIAugiQAAF0mEiSr6pqqumwSrwUAwMZgRhIAgC6CJAAAXcYOklW1o6qurKpDVXVrVb1mGoUBADBsPTOSb0pyTpLnJHl6kr1JHrPaxlW1vap2Ly9JdvUUCgDAsIwVJKtqZ5JfTHJha+2DrbVPJ3lxkoU1drs4yYEVy/7OWgEAGJBxZyQfluReSf5leUVr7Y4kN66xzyVJ9qxYTh3zmAAADNBaM4kT0Vo7nOTw8uOqmvYhAQCYgXFnJL+Y5DtJnrC8oqrum+QHJ1kUAADDN9aMZGvtUFVdkeRNVXV7kq8leX2So9MoDgCA4eo5tX1Rkp1J3pvkYJI3Z3TtIwAAW8jYQbK1dijJi5aWZW+aWEUAAGwIvtkGAIAugiQAAF0ESQAAugiSAAB0ESQBAOgiSAIA0EWQBACgiyAJAEAXQRIAgC6CJAAAXQRJAAC6CJIAAHQRJGHAdmxfmHcJMGjGCKzfNMZLtdYm/qJrHrBqd5ID133upuzctXumx4aNZMf2hZxxvx3zLgMGb99td+bOw0fmXQYM2rjvKYuLi9mzZ0+S7GmtLa623dz+lHvEQ/Zk925BEoB7xh9cMD/OCcBAHbrrSI4cne0ZAwA2p4VtlZ0nTz72CZIwQIfuOpKrP3n7vMsAYBM596xTJh4m3WwDA2QmEoBJm8Z7iyAJAEAXQRIAgC6CJAAAXQRJAAC6CJIAAHQRJAEA6CJIAgDQRZAEAKCLIAkAQBdBEgCALoIkAABduoJkVW2rql+vqi9U1eGquqmqfmPSxQEAMFwLnftdkuSlSS5I8uEkD07yQ8fbsKq2J9m+YtWuzmMCADAgYwfJqtqV5FVJXtlae9fS6i9mFCiP5+Ikv9NXHgAAQ9VzavuHM5ph/OA6t78kyZ4Vy6kdxwQAYGB6Tm3fNc7GrbXDSQ4vP66qjkMCADA0PTOSn88oTD5twrUAALCBjD0j2Vr7VlW9Ickbq+rbST6S5P5JHtlau2LSBQIAMEy9d22/LsmRJL+X5CFJbk3y9kkVBQDA8HUFydba0SSvX1oAANiCfLMNAABdBEkAALoIkgAAdBEkAQDoIkgCANBFkAQAoIsgCQBAF0ESAIAugiQAAF0ESQAAugiSAAB0ESQBAOgiSMIALWyreZcAwCYzjfeWhYm/InCP7Tx5IeeedUqOHG3zLgWATWBhW2XnyZOPfYIkDNQ0BjwATJJ3KhioQ3cdMSMJwESYkYQt5NBdR3L1J2+fdxkAbCLnnnXKxMOkm21ggMxEAjBp03hvESQBAOgiSAIA0EWQBACgiyAJAEAXQRIAgC6CJAAAXQRJAAC6CJIAAHQZK0hW1TVVddm0igEAYOMwIwkAQBdBEgCALj1BcqGq3lZVB6rqtqp6XVXVxCsDAGDQeoLki5McSfKjSV6V5NeSnL/axlW1vap2Ly9JdnVVCgDAoPQEyZuTXNBau7G19u4kf5zkgjW2vzjJgRXL/o5jAgAwMD1B8vrWWlvx+LokD6+qk1bZ/pIke1Ysp3YcEwCAgVmY9gFaa4eTHF5+7HJKAIDNoWdG8gnHPH5iks+31r47gXoAANggeoLk6VV1aVWdWVU/l+RXk7x1wnUBADBwPae2r0xycpKPJvluRiHyTyZZFAAAwzdWkGyt7V3x8BWTLQUAgI3EN9sAANBFkAQAoIsgCQBAF0ESAIAugiQAAF0ESQAAugiSAAB0ESQBAOgiSAIA0EWQBACgiyAJAEAXQRIAgC6CJAzQwraadwkAbDLTeG9ZmPgrAvfYzpMXcu5Zp+TI0TbvUgDYBBa2VXaePPnYN7cg+dlbDmTnQW+SsJod2xdyxv12zLsMAFjV3ILk899xfbZtv/e8Dg8bwocu3CtMAjBYrpGEAbvz8JF5lwAAqxIkAQDoIkgCANBFkAQAoIsgCQBAF0ESAIAugiQAAF0ESQAAugiSAAB0ESQBAOgiSAIA0EWQBACgiyAJAECXsYNkVT2zqj5cVd+oqtur6m+r6mFrbL+9qnYvL0l23aOKAQAYhJ4ZyR1JLk3yuCRPS3I0yV9X1WqvdXGSAyuW/R3HBABgYBbG3aG19lcrH1fVS5L8d5JHJPnMcXa5JKPguWxXhEkAgA2v59T2w6vqPVX1n1W1mOS/lp46/Xjbt9YOt9YWl5ckB/vLBQBgKMaekUzy3iRfSvLSJLdkFEY/k+ReE6wLAICBGytIVtUpSc5M8tLW2j8vrXvyNAoDAGDYxp2R/HqS25O8rKpuzeh09h9OvCoAAAZvrGskW2tHk7wgyWMzOp39liQXTaEuAAAGrueu7Q9kdIf2SjWZcgAA2Ch8sw0AAF0ESQAAugiSAAB0ESQBAOgiSAIA0EWQBACgiyAJAEAXQRIAgC6CJAAAXQRJAAC6jP0ViZNy9PA353Vo2DAOHVzM4qJvIAVgthYXF9e1XbXWplzKMQes+v4k+2Z6UAAAepzaWvvyak/OY0byjqWfpyY5OIfj8//tSrI/ejIkejJM+jI8ejI8ejI8vT3ZleSWtTaY26ntJAdba+ubN2Wqqv731KmeDISeDJO+DI+eDI+eDM896MkJt3WzDQAAXQRJAAC6zCNIHk7yu0s/GQY9GR49GSZ9GR49GR49GZ6p9WTmd20DALA5OLUNAEAXQRIAgC6CJAAAXQRJAAC6CJIAAHSZaJCsqqdU1Xur6paqalX13HXss7eqbqiqw1X1hao6b5I1bXXj9mSpH+04y4NmVfNmV1UXV9W/VtXBqvpaVV1VVWeuY7+frar/qKpvVdWnq+qnZ1HvVtHTl6o67zhj5Vuzqnmzq6pXVNWnqmpxabmuqn7qBPsYJ1M0bk+Mkdmrqtcu/TtfdoLtJjJWJj0juSPJJ5P8yno2rqozkrwvyYeSnJ3ksiTvrKpnTLiurWysnqxwZpIHr1i+NuG6trJzklye5IlJzk3yPUn+vqp2rLZDVT0pyXuSXJHkR5JcleSqqnrU9MvdMsbuy5LF3H2sfN80i9xi9id5bZLHJnlckn9I8jdV9cjjbWyczMRYPVlijMxIVT0+yS8l+dQJtpvYWJna50hWVUvyvNbaVWts84Ykz2qtPWrFur9Icp/W2jOnUtgWts6e7M0o2N+3tfaNWdW2lVXV/TMK6ue01v5plW3+MsmO1tqzV6y7PsknWmsvn02lW8s6+3Jekstaa/eZZW1bWVXdkeSi1toVx3nOOJmDE/TkvBgjM1FVO5PckOSXk/xmRv/fv3qVbSc2VuZ9jeSPJfnAMevev7Se+fpEVd1aVVdX1Y/Pu5hNbs/SzzvW2MZYmb319CVJdlbVl6rq5qo60cwMnarqpKp6QUZnWa5bZTPjZIbW2ZPEGJmVy5O8r7V27Bg4nomNlYVxd5iwByX56jHrvppkd1Wd3Fq7aw41bXW3Jnl5ko8l2Z7k/CTXVNUTWms3zLWyTaiqtmV0ScdHWmufWWPT1caKa1enYIy+3JjkJRmdRtqT5MIk11bVI1tr+6df6eZXVY/OKKR8b5JDGZ1V+ewqmxsnMzBmT4yRGVgK9I9J8vh17jKxsTLvIMnAtNZuzGjgL7u2qh6W5IIkL5pPVZva5UkeleTJ8y6Eu1lXX1pr12XFTExVXZvkcxldo/Rb0yxwC7kxo2vo9yT5mSTvqqpz1gguTN+6e2KMTF9VnZbkrUnOba3N/EameQfJryR54DHrHphk0WzkoHw0gs7EVdXbkjw7yVPW8Zf5amPlK9OobSsbsy9301r7TlX9W5IfmEpxW1Br7dtJvrD08ONLNxO8KqMgcizjZAbG7Mmx+xojk/fYJA9IckNVLa87KclTquqVSba31r57zD4TGyvzvkbyuiRPO2bduVn7Wgtm7+yMTnkzATXytiTPS/LU1tq+dexmrExZZ1+OfY2Tkjw6xss0bcvospvjMU7mY62e3I0xMhUfzOjf9OwVy8eSvDvJ2ccJkckEx8pEZySX7hha+VfGGVV1dpI7Wms3VdUlSR7aWvv5peffnuSVVfXGJH+W5KlJnp/kWZOsaysbtydV9eok+5L8e0bXv5yfUV+ePtvKN7XLk7wwyXOSHKz/+4zOA8sz8VV1ZZIvt9YuXnrurUn+sapek9FHZr0go4/eeNlMK9/cxu5LVf12kuszmp25T5KLMvpok3fOuPZNaen3098luSnJroz6szfJM5aeN05mbNyeGCPT11o7mORu13JX1Z1Jbl++xnuaY2XSp7Yfl9FHxyy7dOnnu5Kcl9HnR52+/GRrbV9VPSvJWzKaFt+f5PzW2vsnXNdWNlZPktwryZuTPDTJNzO6QPonW2srX4N75hVLP685Zv0vJPnzpf8+PcnR5Sdaa9dW1QuT/H6SP0jy+STPPcGNIIxn7L4kuW+SP83oAvWvJ/l4kie5fm9iHpDkyox+Tx3I6PfRM1prVy89b5zM3lg9iTEyFFMbK1P7HEkAADa3eV8jCQDABiVIAgDQRZAEAKCLIAkAQBdBEgCALoIkAABdBEkAALoIkgAAdBEkAQDoIkgCANBFkAQAoMv/AJtc1uccB2U0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = tn.plot_longitudinal(communities=com_sn,height=200)\n",
    "plot = tn.plot_longitudinal(communities=com_ig,height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also plot a graph with nodes color corresponding to communities.\n",
    "In this example, we create a dynamic graph with a fix structure, and plot the communities we defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_toy = tn.DynGraphIG()\n",
    "graph_toy.add_interaction(\"a\",\"b\",(1,4))\n",
    "graph_toy.add_interaction(\"a\",\"c\",(1,4))\n",
    "graph_toy.add_interaction(\"a\",\"d\",(1,4))\n",
    "graph_toy.add_interaction(\"b\",\"d\",(1,4))\n",
    "\n",
    "plot = tn.plot_as_graph(graph_toy,com_ig,[1,2,3],auto_show=True,width=200,height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conversion'></a>\n",
    "\n",
    "## Conversion between snapshots and interval representation\n",
    "Dynamic network representations can be converted by calling the appropriate function.\n",
    "  * When converting to interval graphs, we provide the duration of each snapshots\n",
    "  * When converting to snapshots, we provide the slices to which each snapshot should correspond. Note that it is tehrefore possible to have snapshots corresponding to overlapping periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'com1': {'a': [1,2[ , 'd': [1,2[ [3,4[ }, 'com2': {'c': [2,4[ , 'b': [2,4[ }}\n",
      "{'com1': {'a': [1,2[ , 'd': [1,2[ [3,4[ }, 'com2': {'c': [2,4[ , 'b': [2,4[ }}\n"
     ]
    }
   ],
   "source": [
    "converted_ig = com_sn.to_DynCommunitiesIG(1)\n",
    "print(converted_ig.communities())\n",
    "print(com_ig.communities())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'com1': {'a': [1], 'd': [1, 3]}, 'com2': {'b': [2, 3], 'c': [2, 3]}}\n",
      "{'com1': {'a': [1], 'd': [1, 3]}, 'com2': {'c': [2, 3], 'b': [2, 3]}}\n"
     ]
    }
   ],
   "source": [
    "converted_sn = com_ig.to_DynCommunitiesSN(slices=[(x,x+1) for x in range(1,4)])\n",
    "print(converted_sn.communities())\n",
    "print(com_sn.communities())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='slicing'></a>\n",
    "\n",
    "## Slicing\n",
    "Slicing part of networks can be useful, for instance to visualize only a fraction of a large dynamic partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sliced = com_ig.slice(start=1,end=3)\n",
    "plot = tn.plot_longitudinal(communities=sliced,height=200)"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}