chapter_9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d8b2a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep this function handy for all network visualizations in all chapters\n",
    "\n",
    "def draw_graph(G, show_names=False, node_size=1, font_size=10, edge_width=0.5): \n",
    "\n",
    "    import numpy as np\n",
    "    import networkx as nx\n",
    "    \n",
    "    from IPython.display import SVG \n",
    "    from sknetwork.visualization import svg_graph \n",
    "    from sknetwork.data import Bunch \n",
    "    from sknetwork.ranking import PageRank \n",
    "\n",
    "    adjacency = nx.to_scipy_sparse_matrix(G, nodelist=None, dtype=None, weight='weight', format='csr') \n",
    "\n",
    "    names = np.array(list(G.nodes())) \n",
    "\n",
    "    graph = Bunch() \n",
    "    graph.adjacency = adjacency \n",
    "    graph.names = np.array(names) \n",
    "\n",
    "    pagerank = PageRank() \n",
    "\n",
    "    scores = pagerank.fit_transform(adjacency) \n",
    "\n",
    "    if show_names: \n",
    "        \n",
    "        image = svg_graph(graph.adjacency, font_size=font_size, node_size=node_size, names=graph.names, width=700, height=500, scores=scores, edge_width=edge_width) \n",
    "\n",
    "    else: \n",
    "\n",
    "        image = svg_graph(graph.adjacency, node_size=node_size, width=700, height=500, scores = scores, edge_width=edge_width) \n",
    "\n",
    "    return SVG(image) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6fe3432",
   "metadata": {},
   "source": [
    "# get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f43ae8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx \n",
    " \n",
    "G = nx.les_miserables_graph() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb687124",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    " \n",
    "df = nx.to_pandas_edgelist(G)[['source', 'target']] # dropping 'weight' \n",
    "\n",
    "G = nx.from_pandas_edgelist(df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ee23cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Graph with 77 nodes and 254 edges'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.info(G) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a513b3b5",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_graph(G, font_size=12, show_names=True, node_size=4, edge_width=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92ca3fd4",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_graph(nx.k_core(G, 2), font_size=12, show_names=False, node_size=4, edge_width=0.5) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3986a986",
   "metadata": {},
   "source": [
    "# exploring connected components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eaa3d98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_copy = G.copy() \n",
    "\n",
    "G_copy.remove_nodes_from(['Valjean', 'Marius', 'Fantine', 'Cosette', 'Bamatabois']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c1bf97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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x=\"325\" y=\"144\">Dahlia</text><text font-size=\"12\" x=\"371\" y=\"141\">Zephine</text><text font-size=\"12\" x=\"423\" y=\"410\">Perpetue</text><text font-size=\"12\" x=\"316\" y=\"297\">Eponine</text><text font-size=\"12\" x=\"231\" y=\"296\">Anzelma</text><text font-size=\"12\" x=\"279\" y=\"264\">Magnon</text><text font-size=\"12\" x=\"285\" y=\"239\">Pontmercy</text><text font-size=\"12\" x=\"321\" y=\"252\">Boulatruelle</text><text font-size=\"12\" x=\"279\" y=\"297\">Brujon</text><text font-size=\"12\" x=\"404\" y=\"215\">LtGillenormand</text><text font-size=\"12\" x=\"483\" y=\"388\">Gribier</text><text font-size=\"12\" x=\"346\" y=\"215\">MmePontmercy</text><text font-size=\"12\" x=\"424\" y=\"288\">Mabeuf</text><text font-size=\"12\" x=\"378\" y=\"288\">Courfeyrac</text><text font-size=\"12\" x=\"470\" y=\"250\">MmeBurgon</text><text font-size=\"12\" x=\"555\" y=\"228\">Jondrette</text><text font-size=\"12\" x=\"406\" y=\"297\">Combeferre</text><text font-size=\"12\" 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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_graph(G_copy, font_size=12, show_names=True, node_size=4, edge_width=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bbb4c91c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components = list(nx.connected_components(G_copy)) \n",
    "\n",
    "len(components) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "350dfd28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_copy = nx.k_core(G_copy, 1) \n",
    "\n",
    "components = list(nx.connected_components(G_copy)) \n",
    "\n",
    "len(components) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "57f3be0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"540\" width=\"810.0\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M 720 220 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 20 213 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 20 213 102 107\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 246 520 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 41 408 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 693 391 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 343 20 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 720 220\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 20 213\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 246 520\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 41 408\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 693 391\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 343 20\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 580 79\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 102 107\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 365 279 505 508\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 580 79 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 102 107 20 213\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 102 107 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 505 508 365 279\" stroke=\"gray\" stroke-width=\"0.5\"/><circle cx=\"720\" cy=\"220\" r=\"5.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/><circle cx=\"20\" cy=\"213\" r=\"5.0\" style=\"fill:rgb(92, 123, 229);stroke:black;stroke-width:1.0\"/><circle cx=\"246\" cy=\"520\" r=\"5.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/><circle cx=\"41\" cy=\"408\" r=\"5.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/><circle cx=\"693\" cy=\"391\" r=\"5.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/><circle cx=\"343\" cy=\"20\" r=\"5.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/><circle cx=\"365\" cy=\"279\" r=\"5.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/><circle cx=\"580\" cy=\"79\" r=\"5.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/><circle cx=\"102\" cy=\"107\" r=\"5.0\" style=\"fill:rgb(92, 123, 229);stroke:black;stroke-width:1.0\"/><circle cx=\"505\" cy=\"508\" r=\"5.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/><text font-size=\"10\" x=\"728\" y=\"225\">OldMan</text><text font-size=\"10\" x=\"28\" y=\"218\">MmeMagloire</text><text font-size=\"10\" x=\"254\" y=\"525\">Napoleon</text><text font-size=\"10\" x=\"49\" y=\"413\">Cravatte</text><text font-size=\"10\" x=\"701\" y=\"396\">Champtercier</text><text font-size=\"10\" x=\"351\" y=\"25\">Count</text><text font-size=\"10\" x=\"373\" y=\"284\">Myriel</text><text font-size=\"10\" x=\"588\" y=\"84\">Geborand</text><text font-size=\"10\" x=\"110\" y=\"112\">MlleBaptistine</text><text font-size=\"10\" x=\"513\" y=\"513\">CountessDeLo</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = components[0] \n",
    "\n",
    "G_community = G_copy.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cfd22113",
   "metadata": {},
   "outputs": [
    {
     "data": {
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y=\"141\">MotherInnocent</text><text font-size=\"10\" x=\"260\" y=\"285\">Gavroche</text><text font-size=\"10\" x=\"601\" y=\"407\">Gillenormand</text><text font-size=\"10\" x=\"571\" y=\"467\">MlleGillenormand</text><text font-size=\"10\" x=\"228\" y=\"242\">Enjolras</text><text font-size=\"10\" x=\"187\" y=\"281\">Bossuet</text><text font-size=\"10\" x=\"372\" y=\"230\">Gueulemer</text><text font-size=\"10\" x=\"390\" y=\"241\">Babet</text><text font-size=\"10\" x=\"384\" y=\"215\">Claquesous</text><text font-size=\"10\" x=\"357\" y=\"248\">Montparnasse</text><text font-size=\"10\" x=\"88\" y=\"190\">Toussaint</text><text font-size=\"10\" x=\"52\" y=\"24\">Perpetue</text><text font-size=\"10\" x=\"424\" y=\"235\">Eponine</text><text font-size=\"10\" x=\"559\" y=\"222\">Anzelma</text><text font-size=\"10\" x=\"599\" y=\"309\">Magnon</text><text font-size=\"10\" x=\"618\" y=\"332\">Pontmercy</text><text font-size=\"10\" x=\"545\" y=\"321\">Boulatruelle</text><text font-size=\"10\" x=\"399\" y=\"268\">Brujon</text><text font-size=\"10\" x=\"517\" y=\"456\">LtGillenormand</text><text font-size=\"10\" x=\"290\" y=\"66\">Gribier</text><text font-size=\"10\" x=\"649\" y=\"409\">MmePontmercy</text><text font-size=\"10\" x=\"285\" y=\"238\">Mabeuf</text><text font-size=\"10\" x=\"249\" y=\"260\">Courfeyrac</text><text font-size=\"10\" x=\"124\" y=\"387\">MmeBurgon</text><text font-size=\"10\" x=\"27\" y=\"453\">Jondrette</text><text font-size=\"10\" x=\"216\" y=\"289\">Combeferre</text><text font-size=\"10\" x=\"154\" y=\"255\">Prouvaire</text><text font-size=\"10\" x=\"221\" y=\"271\">Feuilly</text><text font-size=\"10\" x=\"190\" y=\"251\">Bahorel</text><text font-size=\"10\" x=\"180\" y=\"266\">Joly</text><text font-size=\"10\" x=\"153\" y=\"286\">Grantaire</text><text font-size=\"10\" x=\"205\" y=\"364\">Child1</text><text font-size=\"10\" x=\"144\" y=\"351\">Child2</text><text font-size=\"10\" x=\"127\" y=\"266\">MmeHucheloup</text><text font-size=\"10\" x=\"727\" y=\"427\">BaronessT</text><text font-size=\"10\" x=\"483\" y=\"524\">MlleVaubois</text><text font-size=\"10\" x=\"416\" y=\"160\">MotherPlutarch</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = components[1] \n",
    "\n",
    "G_community = G_copy.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd06b273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"540\" width=\"800.0\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M 165 20 591 26\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 165 20 720 334\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 165 20 20 323\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 165 20 364 520\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 591 26 165 20\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 591 26 720 334\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 591 26 20 323\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 591 26 364 520\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 720 334 165 20\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 720 334 591 26\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 720 334 20 323\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 720 334 364 520\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 20 323 165 20\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 20 323 591 26\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 20 323 720 334\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 20 323 364 520\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 364 520 165 20\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 364 520 591 26\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 364 520 720 334\" stroke=\"gray\" stroke-width=\"0.5\"/><path d=\"M 364 520 20 323\" stroke=\"gray\" stroke-width=\"0.5\"/><circle cx=\"165\" cy=\"20\" r=\"4.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/><circle cx=\"591\" cy=\"26\" r=\"4.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/><circle cx=\"720\" cy=\"334\" r=\"4.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/><circle cx=\"20\" cy=\"323\" r=\"4.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/><circle cx=\"364\" cy=\"520\" r=\"4.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/><text font-size=\"10\" x=\"172\" y=\"24\">Brevet</text><text font-size=\"10\" x=\"598\" y=\"30\">Judge</text><text font-size=\"10\" x=\"727\" y=\"338\">Chenildieu</text><text font-size=\"10\" x=\"27\" y=\"327\">Cochepaille</text><text font-size=\"10\" x=\"371\" y=\"524\">Champmathieu</text></svg>"
      ],
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       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = components[2] \n",
    "\n",
    "G_community = G_copy.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4253ae38",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = components[3] \n",
    "\n",
    "G_community = G_copy.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=4) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96219acb",
   "metadata": {},
   "source": [
    "# Louvain Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b8ffe61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import community as community_louvain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "60d1b9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_partition(G, partition): \n",
    "\n",
    "    import matplotlib.cm as cm \n",
    "    import matplotlib.pyplot as plt \n",
    "\n",
    "    # draw the graph \n",
    "    plt.figure(3,figsize=(12,12)) \n",
    "    pos = nx.spring_layout(G) \n",
    "\n",
    "    # color the nodes according to their partition \n",
    "    cmap = cm.get_cmap('jet', max(partition.values()) + 1) \n",
    "    nx.draw_networkx_nodes(G, pos, partition.keys(), node_size=40, cmap=cmap, node_color=list(partition.values())) \n",
    "    nx.draw_networkx_edges(G, pos, alpha=0.5, width=0.3) \n",
    "\n",
    "    return plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2542413d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "partition = community_louvain.best_partition(G, resolution=1) \n",
    "\n",
    "draw_partition(G, partition) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e119aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Napoleon': 0,\n",
       " 'Myriel': 0,\n",
       " 'MlleBaptistine': 0,\n",
       " 'MmeMagloire': 0,\n",
       " 'CountessDeLo': 0,\n",
       " 'Geborand': 0,\n",
       " 'Champtercier': 0,\n",
       " 'Cravatte': 0,\n",
       " 'Count': 0,\n",
       " 'OldMan': 0,\n",
       " 'Valjean': 5,\n",
       " 'Labarre': 5,\n",
       " 'Marguerite': 2,\n",
       " 'MmeDeR': 5,\n",
       " 'Isabeau': 5,\n",
       " 'Gervais': 5,\n",
       " 'Fantine': 2,\n",
       " 'MmeThenardier': 3,\n",
       " 'Thenardier': 3,\n",
       " 'Cosette': 4,\n",
       " 'Javert': 3,\n",
       " 'Fauchelevent': 5,\n",
       " 'Bamatabois': 5,\n",
       " 'Simplice': 2,\n",
       " 'Scaufflaire': 5,\n",
       " 'Woman1': 5,\n",
       " 'Judge': 5,\n",
       " 'Champmathieu': 5,\n",
       " 'Brevet': 5,\n",
       " 'Chenildieu': 5,\n",
       " 'Cochepaille': 5,\n",
       " 'Woman2': 4,\n",
       " 'MotherInnocent': 5,\n",
       " 'Gavroche': 1,\n",
       " 'Gillenormand': 4,\n",
       " 'MlleGillenormand': 4,\n",
       " 'Marius': 4,\n",
       " 'Enjolras': 1,\n",
       " 'Bossuet': 1,\n",
       " 'Gueulemer': 3,\n",
       " 'Babet': 3,\n",
       " 'Claquesous': 3,\n",
       " 'Montparnasse': 3,\n",
       " 'Toussaint': 4,\n",
       " 'Listolier': 2,\n",
       " 'Tholomyes': 2,\n",
       " 'Fameuil': 2,\n",
       " 'Blacheville': 2,\n",
       " 'Favourite': 2,\n",
       " 'Dahlia': 2,\n",
       " 'Zephine': 2,\n",
       " 'Perpetue': 2,\n",
       " 'Eponine': 3,\n",
       " 'Anzelma': 3,\n",
       " 'Magnon': 4,\n",
       " 'Pontmercy': 4,\n",
       " 'Boulatruelle': 3,\n",
       " 'Brujon': 3,\n",
       " 'LtGillenormand': 4,\n",
       " 'Gribier': 5,\n",
       " 'MmePontmercy': 4,\n",
       " 'Mabeuf': 1,\n",
       " 'Courfeyrac': 1,\n",
       " 'MmeBurgon': 1,\n",
       " 'Jondrette': 1,\n",
       " 'Combeferre': 1,\n",
       " 'Prouvaire': 1,\n",
       " 'Feuilly': 1,\n",
       " 'Bahorel': 1,\n",
       " 'Joly': 1,\n",
       " 'Grantaire': 1,\n",
       " 'Child1': 1,\n",
       " 'Child2': 1,\n",
       " 'MmeHucheloup': 1,\n",
       " 'BaronessT': 4,\n",
       " 'MlleVaubois': 4,\n",
       " 'MotherPlutarch': 1}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7cfcbefd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Marguerite',\n",
       " 'Fantine',\n",
       " 'Simplice',\n",
       " 'Listolier',\n",
       " 'Tholomyes',\n",
       " 'Fameuil',\n",
       " 'Blacheville',\n",
       " 'Favourite',\n",
       " 'Dahlia',\n",
       " 'Zephine',\n",
       " 'Perpetue']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[node for node, community in partition.items() if community == 2] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37cb7b66",
   "metadata": {},
   "source": [
    "# Label Propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "133870ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms.community.label_propagation import label_propagation_communities "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9378bc6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "communities = label_propagation_communities(G) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ecbde5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Champtercier',\n",
       " 'Count',\n",
       " 'CountessDeLo',\n",
       " 'Cravatte',\n",
       " 'Geborand',\n",
       " 'Myriel',\n",
       " 'Napoleon',\n",
       " 'OldMan'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities = list(communities) \n",
    "\n",
    "communities[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aa9503fd",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[1] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6f68a19f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[2] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "23e889b1",
   "metadata": {},
   "outputs": [
    {
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x=\"370\" y=\"76\">Gueulemer</text><text font-size=\"10\" x=\"273\" y=\"335\">MmeThenardier</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[3] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a3de85",
   "metadata": {},
   "source": [
    "# Girvan-Newman Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4758e06d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms.community import girvan_newman "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6cd35578",
   "metadata": {},
   "outputs": [],
   "source": [
    "communities = girvan_newman(G) \n",
    "\n",
    "communities = list(communities) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "31e6d034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(communities) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5ca548c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "communities = communities[9] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d34ccbca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[0] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "070e19cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[1] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d6de96db",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[2] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a19d48c1",
   "metadata": {},
   "outputs": [
    {
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       "<IPython.core.display.SVG object>"
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[3] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7a7ca1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community = communities[4] \n",
    "\n",
    "G_community = G.subgraph(community) \n",
    "\n",
    "draw_graph(G_community, show_names=True, node_size=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aa9c06f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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