{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Game of Thrones tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Game of Thrones (GoT) is an American fantasy drama TV series, created by D. Benioff and D.B. Weiss for the American television network HBO. It is the screen adaption of the series of fantasy novels *A Song of Ice and Fire*, written by George R.R. Martin. The series premiered on HBO in the United States on April 17, 2011, and concluded on May 19, 2019, with 73 episodes broadcast over eight seasons. With its 12 million viewers during season 8 and a plethora of awards---according to [Wikipedia](https://en.wikipedia.org/wiki/Game_of_Thrones), Game of Thrones has attracted record viewership on HBO and has a broad, active, and international fan base. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The intricate world narrated by George R.R. Martin and scripted by Benioff and Weiss has stimulated the curiosity of ranks of scientists, delighted by the opportunity to study complex social phenomena. In this notebook, we delve into the study of GoT relationships to discover what the hypergraphs they generate reveal about the story."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we replicate some of the analysis you can read in our paper at this [link](https://www.internetmathematicsjournal.com/article/12464-analyzing-exploring-and-visualizing-complex-networks-via-hypergraphs-using-simplehypergraphs-jl)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What we need... installing and loading packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Pkg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pkg\"add PyCall Conda SimpleHypergraphs PyPlot GraphPlot ColorSchemes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prerequisites for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PyCall\n",
    "using Conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conda.runconda(`install matplotlib --yes`)\n",
    "#Conda.runconda(`install networkx --yes`)\n",
    "#run(`$(PyCall.python) -m pip install hypernetx==1.2.5`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using SimpleHypergraphs\n",
    "using Graphs\n",
    "using PyPlot\n",
    "using GraphPlot, ColorSchemes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data set\n",
    "This study is based on the dataset at the GitHub repository of Jeffrey Lancaster [Game of Thrones Datasets and Visualizations](https://github.com/jeffreylancaster/game-of-thrones). We will thus focus on the GoT TV series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We studied GoT characters' co-occurrences with different levels of granularity. We modeled the GoT data set building three different types of hypergraphs, each one reporting whether the GoT characters have appeared in the same **season**, in the same **episode**, or in the same **scene** together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypergraph with each *season* as an edge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we load a hypergraph studying characters' co-occurences within seasons. Here, the hyperedges are the GoT seasons and the characters who appear in each eason are the nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pth = joinpath(dirname(pathof(SimpleHypergraphs)),\"..\",\"tutorials\", \"basics\", \"data\", \"hg_seasons_all.json\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = SimpleHypergraphs.hg_load(pth; format=JSON_Format(), T=Bool, V=Symbol, E=Symbol);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "577"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many characters did we see during the overall TV series?\n",
    "size(h)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Season 1 has 125 characters\n",
      "Season 2 has 137 characters\n",
      "Season 3 has 137 characters\n",
      "Season 4 has 152 characters\n",
      "Season 5 has 175 characters\n",
      "Season 6 has 208 characters\n",
      "Season 7 has 75 characters\n",
      "Season 8 has 66 characters\n"
     ]
    }
   ],
   "source": [
    "# how many characters does each season have?\n",
    "# we can ask this way...\n",
    "map(he -> println(\"Season $he has $(length(getvertices(h, he))) characters\"), 1:nhe(h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-element Vector{Int64}:\n",
       " 125\n",
       " 137\n",
       " 137\n",
       " 152\n",
       " 175\n",
       " 208\n",
       "  75\n",
       "  66"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ... or this way\n",
    "length.(h.he2v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SimpleHypergraphs** integates the Python library **HyperNetX** to let the user visualize a hypergraph `h` exploiting an Euler-diagram visualization. For more details, please refer to the library [HyperNetX](https://github.com/pnnl/HyperNetX)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pth = joinpath(dirname(pathof(SimpleHypergraphs)),\"..\",\"tutorials\", \"basics\", \"data\", \"hg_seasons_min.json\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's visualize (a smaller parte of) the hypergraph we built\n",
    "# To build this smaller hypergraph, we considered only those characters \n",
    "# appearing at least in 10 scenes in the whole series\n",
    "h1 = SimpleHypergraphs.hg_load(pth; format=JSON_Format(), T=Int, V=Symbol, E=Symbol);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Season 1 has *69* characters appearing in at least 10 scenes\n",
      "Season 2 has *74* characters appearing in at least 10 scenes\n",
      "Season 3 has *83* characters appearing in at least 10 scenes\n",
      "Season 4 has *83* characters appearing in at least 10 scenes\n",
      "Season 5 has *75* characters appearing in at least 10 scenes\n",
      "Season 6 has *98* characters appearing in at least 10 scenes\n",
      "Season 7 has *56* characters appearing in at least 10 scenes\n",
      "Season 8 has *44* characters appearing in at least 10 scenes\n"
     ]
    }
   ],
   "source": [
    "map(he -> \n",
    "    println(\"Season $he has *$(length(getvertices(h1, he)))* characters appearing in at least 10 scenes\"), \n",
    "    1:nhe(h));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-element Vector{Int64}:\n",
       " 69\n",
       " 74\n",
       " 83\n",
       " 83\n",
       " 75\n",
       " 98\n",
       " 56\n",
       " 44"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length.(h1.he2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# viz params: edge labels\n",
    "edge_labels = Dict{Int, String}(map(x -> x=>\"S$x\", 1:nhe(h)))\n",
    "edge_labels_kwargs = Dict{String,Any}(\"fontsize\" => \"x-large\")\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 700x700 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SimpleHypergraphs.draw(\n",
    "    h1, \n",
    "    HyperNetX;\n",
    "    no_border = true,\n",
    "    width = 7,\n",
    "    height = 7,\n",
    "    collapse_nodes = true, \n",
    "    with_node_counts = true, \n",
    "    with_node_labels = true,\n",
    "    edge_labels = edge_labels, \n",
    "    edge_labels_kwargs = edge_labels_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White_Walker\n",
      "Jon_Snow\n",
      "Sansa_Stark\n",
      "Arya_Stark\n",
      "Theon_Greyjoy\n",
      "Cersei_Lannister\n",
      "Jaime_Lannister\n",
      "Tyrion_Lannister\n",
      "Daenerys_Targaryen\n",
      "Jorah_Mormont\n",
      "Drogon\n",
      "Rhaegal\n",
      "Viserion\n",
      "Lord_Varys\n",
      "Samwell_Tarly\n",
      "Bronn\n"
     ]
    }
   ],
   "source": [
    "# who are the characters appearing in all 8 seasons?\n",
    "most_important_character_ids = findall(x->x==1, (length.(h.v2he) .== 8))\n",
    "\n",
    "for id in most_important_character_ids\n",
    "    println(SimpleHypergraphs.get_vertex_meta(h, id))\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypergraph with each scene as an edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's have a closer look of what's happening in season 1\n",
    "pth = joinpath(dirname(pathof(SimpleHypergraphs)),\"..\",\"tutorials\", \"basics\", \"data\", \"hg_season1.json\");\n",
    "hg = SimpleHypergraphs.hg_load(pth; format=JSON_Format(), T=Bool, V=Symbol, E=Symbol);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"125 characters and 286 scenes\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many characters do we have? How many scenes?\n",
    "\"$(nhv(hg)) characters and $(nhe(hg)) scenes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The collaboration structure of Game of Thrones.\n",
    "At this point, we had an overview about how many characters appeared over the whole GoT TV series and which one of them made it till the end.\n",
    "\n",
    "Let's find out how these characters interacted with each other in season 1. To gather insights within these complex relationships, we run a community detection algorithm on the hypergraph built considering scenes as hyperedges.\n",
    "\n",
    "Running this algorithm, we expect to find coherent plotlines and, therefore, groups of characters frequently appearing in a scene together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Vector{Vector{Int64}}:\n",
       " [1, 5, 3, 4, 2, 16, 12, 29, 34, 49  …  37, 87, 48, 44, 40, 106, 93, 101, 118, 119]\n",
       " [99]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's assure to have a single connected component\n",
    "# Otherwise, we pick the largest one\n",
    "cc = get_connected_components(hg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-element Vector{Vector{Int64}}:\n",
       " [1, 5, 3, 4, 2, 16, 12, 29, 34, 49  …  37, 87, 48, 44, 40, 106, 93, 101, 118, 119]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_vertex!(hg, 99)\n",
    "cc = get_connected_components(hg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"We found 18 communities in the hypergraph of the 1st season.\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We used the label propagation (LP) algorithm\n",
    "cflp = CFLabelPropagationFinder(100,1234)\n",
    "\n",
    "comms = findcommunities(hg, cflp)\n",
    "\n",
    "\"We found $(length(comms.np)) communities in the hypergraph of the 1st season.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-element Vector{Int64}:\n",
       "  1\n",
       "  1\n",
       "  4\n",
       " 37\n",
       "  1\n",
       "  1\n",
       "  1\n",
       " 18\n",
       "  3\n",
       "  1\n",
       " 16\n",
       "  4\n",
       " 13\n",
       "  7\n",
       " 12\n",
       "  1\n",
       "  1\n",
       "  2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length.(comms.np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White_Walker\n",
      "-----\n",
      "Ros\n",
      "-----\n",
      "Shaggydog\n",
      "Nymeria\n",
      "Grey_Wind\n",
      "Lady\n",
      "-----\n",
      "Loras_Tyrell\n",
      "Renly_Baratheon\n",
      "Grand_Maester_Pycelle\n",
      "Septa_Mordane\n",
      "Old_Nan\n",
      "Mycah\n",
      "Hugh_of_the_Vale\n",
      "Sansa_Stark\n",
      "Hodor\n",
      "Joffrey_Baratheon\n",
      "Gregor_Clegane\n",
      "Jon_Arryn\n",
      "Jory_Cassel\n",
      "Royal_Steward\n",
      "Myrcella_Baratheon\n",
      "Benjen_Stark\n",
      "Rickon_Stark\n",
      "Varly\n",
      "Eddard_Stark\n",
      "Robert_Baratheon\n",
      "Beric_Dondarrion\n",
      "Lancel_Lannister\n",
      "Ilyn_Payne\n",
      "Sandor_Clegane\n",
      "Tommen_Baratheon\n",
      "Cersei_Lannister\n",
      "Joss\n",
      "Janos_Slynt\n",
      "Yoren\n",
      "Petyr_Baelish\n",
      "Arya_Stark\n",
      "Lord_Varys\n",
      "Barristan_Selmy\n",
      "Summer\n",
      "Tomard\n",
      "Jaime_Lannister\n",
      "Meryn_Trant\n",
      "-----\n",
      "Mhaegen\n",
      "-----\n",
      "Armeca\n",
      "-----\n",
      "Tobho_Mott\n",
      "-----\n",
      "Mirri_Maz_Duur\n",
      "Viserys_Targaryen\n",
      "Irri\n",
      "Drogon\n",
      "Viserion\n",
      "Daenerys_Targaryen\n",
      "Rhaegal\n",
      "Mago\n",
      "Khal_Drogo\n",
      "Wine_Merchant\n",
      "Doreah\n",
      "Qotho\n",
      "Jhiqui\n",
      "Rakharo\n",
      "Illyrio_Mopatis\n",
      "Rhaego\n",
      "Jorah_Mormont\n",
      "Dothraki_Crone\n",
      "-----\n",
      "Osha\n",
      "Wallen\n",
      "Stiv\n",
      "-----\n",
      "Three-Eyed_Raven\n",
      "-----\n",
      "Vardis_Egen\n",
      "Addam_Marbrand\n",
      "Leo_Lefford\n",
      "Chella\n",
      "Robin_Arryn\n",
      "Lannister_Messenger\n",
      "Shae\n",
      "Mord\n",
      "Lysa_Arryn\n",
      "Kevan_Lannister\n",
      "Bronn\n",
      "Timett\n",
      "Marillion\n",
      "Tyrion_Lannister\n",
      "Shagga\n",
      "Tywin_Lannister\n",
      "-----\n",
      "Walder_Frey\n",
      "Joyeuse_Erenford\n",
      "Stevron_Frey\n",
      "Ryger_Rivers\n",
      "-----\n",
      "Alliser_Thorne\n",
      "Ghost\n",
      "Rast\n",
      "Jon_Snow\n",
      "Maester_Aemon\n",
      "Jaremy_Rykker\n",
      "Pypar\n",
      "Othell_Yarwyck\n",
      "Jafer_Flowers\n",
      "Othor\n",
      "Jeor_Mormont\n",
      "Samwell_Tarly\n",
      "Grenn\n",
      "-----\n",
      "Hot_Pie\n",
      "Syrio_Forel\n",
      "Lommy_Greenhands\n",
      "Gendry\n",
      "King's_Landing_Baker\n",
      "Street_Urchin\n",
      "Red_Keep_Stableboy\n",
      "-----\n",
      "Robb_Stark\n",
      "Jonos_Bracken\n",
      "Theon_Greyjoy\n",
      "Will\n",
      "Lannister_Scout\n",
      "Bran_Stark\n",
      "Greatjon_Umber\n",
      "Catspaw_Assassin\n",
      "Rodrik_Cassel\n",
      "Catelyn_Stark\n",
      "Maester_Luwin\n",
      "Rickard_Karstark\n",
      "-----\n",
      "Wight_Wildling_Girl\n",
      "-----\n",
      "Little_Bird\n",
      "-----\n",
      "Waymar_Royce\n",
      "Gared\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "# Who are they?\n",
    "for c in comms.np\n",
    "    for character in c\n",
    "       println(get_vertex_meta(hg, character)) \n",
    "    end\n",
    "    println(\"-----\")\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some comments\n",
    "\n",
    "Based on the communities above, we can say that the LP algorithm ran on such hypergraph revealed:\n",
    "* The presence of minor communities of characters, appearing only in few scenes of the whole season. It is interesting to note that the algorithm correctly identifies background characters that do heavily not contribute to the main storyline (for now).\n",
    "    - (Syrio_Forel);\n",
    "    - (Waymar_Royce, White_Walker, Gared); \n",
    "    - (Three-Eyed_Raven);\n",
    "    - (Hot_Pie, Red_Keep_Stableboy, Vayon_Poole, Gendry);\n",
    "    - ...\n",
    " \n",
    "* The other communities embody the different sub-plotlines happening in the first season. We can list:\n",
    "    - two communities related to Daenerys_Targaryen and the Dothraki;\n",
    "    - one community related to the events happening in Castle Black and related to Jon Snow;\n",
    "    - one community related to the Houses Arryn and Frey.\n",
    "    \n",
    "* The last more significant community contains the majority of the characters appearing in the first season. This result makes sense as all these characters have been forced to stay together at the Red Keep. Thus, they appear in many scenes together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How good these communities are in terms of modularity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5040026252111678"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modularity(hg, comms.np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find communities by maximizing the value of modularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"We found 16 communities in the hypergraph of the 1st season with a modularity value of 0.5121486201080864.\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we used a CNM-Like algorithm for finding communities.\n",
    "cnm = CFModularityCNMLike(500)\n",
    "\n",
    "cnm_comms = findcommunities(hg, cnm)\n",
    "\n",
    "\"We found $(length(cnm_comms.bp)) communities in the hypergraph of the 1st season with a modularity value of $(cnm_comms.bm).\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Int64, Int64} with 5 entries:\n",
       "  5  => 1\n",
       "  12 => 1\n",
       "  76 => 1\n",
       "  1  => 12\n",
       "  19 => 1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many characters are there in each community?\n",
    "\n",
    "# size of each community\n",
    "comms_size = length.(cnm_comms.bp)\n",
    "\n",
    "# how many community of that size do exist?\n",
    "values = unique(length.(cnm_comms.bp))\n",
    "data = Dict([(i, count(x->x==i, comms_size)) for i in values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White_Walker\n",
      "Wight_Wildling_Girl\n",
      "Waymar_Royce\n",
      "Will\n",
      "Gared\n",
      "-----\n",
      "Khal_Drogo\n",
      "Wine_Merchant\n",
      "Mirri_Maz_Duur\n",
      "Doreah\n",
      "Viserys_Targaryen\n",
      "Qotho\n",
      "Jhiqui\n",
      "Little_Bird\n",
      "Irri\n",
      "Rakharo\n",
      "Drogon\n",
      "Viserion\n",
      "Daenerys_Targaryen\n",
      "Rhaegal\n",
      "Illyrio_Mopatis\n",
      "Rhaego\n",
      "Jorah_Mormont\n",
      "Dothraki_Crone\n",
      "Mago\n",
      "-----\n",
      "Renly_Baratheon\n",
      "Grand_Maester_Pycelle\n",
      "Nymeria\n",
      "Ros\n",
      "Joyeuse_Erenford\n",
      "Septa_Mordane\n",
      "Alliser_Thorne\n",
      "Hodor\n",
      "Lady\n",
      "Myrcella_Baratheon\n",
      "Rast\n",
      "Jon_Snow\n",
      "Maester_Aemon\n",
      "Eddard_Stark\n",
      "Robert_Baratheon\n",
      "Stevron_Frey\n",
      "Lannister_Scout\n",
      "Lancel_Lannister\n",
      "Pypar\n",
      "Sandor_Clegane\n",
      "Greatjon_Umber\n",
      "Joss\n",
      "Petyr_Baelish\n",
      "Othor\n",
      "Samwell_Tarly\n",
      "Arya_Stark\n",
      "Summer\n",
      "Lord_Varys\n",
      "Barristan_Selmy\n",
      "Wallen\n",
      "Grenn\n",
      "Red_Keep_Stableboy\n",
      "Old_Nan\n",
      "Mycah\n",
      "Hugh_of_the_Vale\n",
      "Sansa_Stark\n",
      "Jon_Arryn\n",
      "Joffrey_Baratheon\n",
      "Robb_Stark\n",
      "Jory_Cassel\n",
      "Gregor_Clegane\n",
      "Ghost\n",
      "Royal_Steward\n",
      "Shaggydog\n",
      "Mhaegen\n",
      "Armeca\n",
      "Rickon_Stark\n",
      "Syrio_Forel\n",
      "Three-Eyed_Raven\n",
      "Varly\n",
      "Stiv\n",
      "Beric_Dondarrion\n",
      "Theon_Greyjoy\n",
      "King's_Landing_Baker\n",
      "Street_Urchin\n",
      "Jaremy_Rykker\n",
      "Ilyn_Payne\n",
      "Osha\n",
      "Bran_Stark\n",
      "Cersei_Lannister\n",
      "Othell_Yarwyck\n",
      "Tommen_Baratheon\n",
      "Janos_Slynt\n",
      "Yoren\n",
      "Marillion\n",
      "Jafer_Flowers\n",
      "Jeor_Mormont\n",
      "Catspaw_Assassin\n",
      "Walder_Frey\n",
      "Rodrik_Cassel\n",
      "Maester_Luwin\n",
      "Grey_Wind\n",
      "Jaime_Lannister\n",
      "Catelyn_Stark\n",
      "Ryger_Rivers\n",
      "Meryn_Trant\n",
      "-----\n",
      "Benjen_Stark\n",
      "-----\n",
      "Lommy_Greenhands\n",
      "-----\n",
      "Lysa_Arryn\n",
      "-----\n",
      "Hot_Pie\n",
      "-----\n",
      "Addam_Marbrand\n",
      "Leo_Lefford\n",
      "Bronn\n",
      "Chella\n",
      "Timett\n",
      "Lannister_Messenger\n",
      "Tyrion_Lannister\n",
      "Shae\n",
      "Shagga\n",
      "Tywin_Lannister\n",
      "Mord\n",
      "Kevan_Lannister\n",
      "-----\n",
      "Tobho_Mott\n",
      "-----\n",
      "Gendry\n",
      "-----\n",
      "Tomard\n",
      "-----\n",
      "Vardis_Egen\n",
      "-----\n",
      "Robin_Arryn\n",
      "-----\n",
      "Jonos_Bracken\n",
      "-----\n",
      "Rickard_Karstark\n",
      "-----\n",
      "Loras_Tyrell\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "# Who are they?\n",
    "for c in cnm_comms.bp\n",
    "    for character in c\n",
    "       println(get_vertex_meta(hg, character)) \n",
    "    end\n",
    "    println(\"-----\")\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's visualize these communities\n",
    "We will visualize the obtained communities through a two-section representation of the hypergraph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{124, 886} undirected simple Int64 graph"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As a TwoSectionView can be instantiated only for hypergraphs with non-overlapping edges,\n",
    "# here we will use Graphs.jl directly.\n",
    "m = get_twosection_adjacency_mx(hg;replace_weights=1)\n",
    "\n",
    "t = Graph(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Just a few viz params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_colors = vcat(ColorSchemes.rainbow[range(1, stop=length(ColorSchemes.rainbow), step=2)], ColorSchemes.rainbow[2]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "function get_color(i, comms, colors)\n",
    "    for j in 1:length(comms)\n",
    "        if i in comms[j]\n",
    "            return colors[j % length(colors) + 1]\n",
    "        end\n",
    "    end\n",
    "    return \"black\"\n",
    "end;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "degrees = [Graphs.degree(t, v) for v in Graphs.vertices(t)];\n",
    "\n",
    "dsize = fill(1.3, 124)\n",
    "dsize += degrees/maximum(degrees);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate comms labels\n",
    "function get_labels(comms)\n",
    "    labels = fill(\"\", 124)\n",
    "    index = 1\n",
    "\n",
    "    for c in comms\n",
    "        labels[first(c)] = \"C$(index)\"\n",
    "        index += 1\n",
    "    end\n",
    "    \n",
    "    labels\n",
    "end\n",
    "\n",
    "labels = get_labels(comms.np)\n",
    "cnm_labels = get_labels(cnm_comms.bp);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Communities from Label Propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
       "     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
       "     version=\"1.2\"\n",
       "     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
       "     stroke=\"none\"\n",
       "     fill=\"#000000\"\n",
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       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}, Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), Compose.UnitBox{Float64, Float64, Float64, Float64}(-1.2, -1.2, 2.4, 2.4, 0.0mm, 0.0mm, 0.0mm, 0.0mm), nothing, nothing, nothing, List([Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}, Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, nothing, List([]), List([Compose.Form{Compose.LinePrimitive}(Compose.LinePrimitive[Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.9350693278501814cx, 0.7019634624572904cy), (0.9151344292780357cx, 0.76914648195973cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.9295049530445835cx, 0.6798507086102292cy), (0.6075990322230381cx, 0.3554163465612441cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.9338255978349737cx, 0.7015260097580378cy), (0.8749736026981128cx, 0.8455279656416788cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.9036247259700854cx, 0.7710938859919211cy), (0.6057108837633347cx, 0.3570150490180286cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.9050207355467965cx, 0.793487305947037cy), (0.8763037010546739cx, 0.8461599472344788cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.6002596759239308cx, 0.3595236196118856cy), (0.75321216530874cx, 0.9874885580974103cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.603921131644458cx, 0.3581283312427364cy), (0.8637316107576426cx, 0.8460977527992061cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.5960978298769989cx, 0.3285899007605423cy), (0.5915558762787608cx, 0.16959534520050615cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.5846699821166366cx, 0.334018278834709cy), (0.4710700602722227cx, 0.23589347938503902cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.586528393568641cx, 0.3321968281855011cy), (0.4790892937413326cx, 0.20264192988316554cy)])  …  Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.8999361777846588cx, -0.41572771453597523cy), (0.8992120692905194cx, -0.545171693967352cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.3466998671999637cx, -0.9866272015923355cy), (0.34087685989024585cx, -0.9287729861451546cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.33911886661862134cx, -0.9899524826827718cy), (0.29333237282142677cx, -0.9384186834151121cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.3595864927598932cx, -0.9931111262616619cy), (0.3924708072764448cx, -0.9734817522169398cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.3264478263988829cx, -0.9184786215263618cy), (0.2974884704151589cx, -0.9252927323090122cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.3500571778677607cx, -0.9237572612028438cy), (0.393485179542571cx, -0.9582358050132479cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.29720795289200264cx, -0.9324623912148043cy), (0.3912089168681676cx, -0.9625016533616813cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.044630415284505266cx, 0.31349745008684726cy), (-0.0156429835903428cx, 0.412438668745422cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.33659290403707326cx, 0.3199384264154481cy), (0.3203464859340747cx, 0.2913928722273098cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.9999616247270962cx, -0.3233071169860603cy), (0.9997307497501278cx, -0.24244695134393476cy)])], Symbol(\"\"))]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[Compose.LineWidthPrimitive(0.26940795304016235mm)]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.8274509803921568,0.8274509803921568,0.8274509803921568,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}, Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[Compose.LineWidthPrimitive(0.26940795304016235mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.8274509803921568,0.8274509803921568,0.8274509803921568,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}, Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.FontSizePrimitive}(Compose.FontSizePrimitive[Compose.FontSizePrimitive(4.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}, Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, nothing, List([]), List([Compose.Form{Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}}(Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}[Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.9388392605299165cx, 0.6892583333176105cy), 0.005301059499619695w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.9113644965983005cx, 0.7818516110994098cy), 0.005301059499619695w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.5965459429061282cx, 0.3442764396839484cy), 0.006277175271504342w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.7562595586702383cx, 1.0cy), 0.005150887842406672w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.8699599400031699cx, 0.857795642082106cy), 0.005301059499619695w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.5909791183608863cx, 0.14940549366750977cy), 0.008079235158060612w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.4562109035906339cx, 0.2230585077961582cy), 0.007853977672241077w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.4665553743549551cx, 0.18752796621405565cy), 0.007853977672241077w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.3988923014353962cx, -0.20675823135727356cy), 0.008980265101338746w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.45461335086184773cx, -0.21099080208373922cy), 0.008229406815273633w)  …  Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.2844054045819402cx, -0.928371166097884cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.40401146517823006cx, -0.9665928784786016cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.9342162095544246cx, -0.2091675860497345cy), 0.005225973671013184w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((-0.04830372687376239cx, 0.30095953610438997cy), 0.005225973671013184w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((-0.011969672001085674cx, 0.4249765827278793cy), 0.005225973671013184w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.3434267315236226cx, 0.3319457126734373cy), 0.005526316985439229w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.3135126584475254cx, 0.2793855859693206cy), 0.005526316985439229w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((1.0cx, -0.3367474255214784cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.9996923744772239cx, -0.22900664280851668cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.0679408169342377cx, -0.1819575869908836cy), 0.0053761453282262055w)], Symbol(\"\"))]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[Compose.LineWidthPrimitive(0.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.250728,0.225386,0.769152,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.250728,0.225386,0.769152,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.513417,0.72992,0.440682,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.36048,0.655759,0.645692,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.857359,0.131106,0.132128,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.863512,0.670771,0.236564,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.513417,0.72992,0.440682,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.513417,0.72992,0.440682,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.705038,0.742591,0.299167,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.513417,0.72992,0.440682,1.0))  …  Compose.FillPrimitive(RGBA{Float64}(0.902853,0.453964,0.192014,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.902853,0.453964,0.192014,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.857359,0.131106,0.132128,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.705038,0.742591,0.299167,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.705038,0.742591,0.299167,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.513417,0.72992,0.440682,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.513417,0.72992,0.440682,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.857359,0.131106,0.132128,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.857359,0.131106,0.132128,1.0)), Compose.FillPrimitive(RGBA{Float64}(0.705038,0.742591,0.299167,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}, Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, nothing, List([]), List([Compose.Form{Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}}(Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}[Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.9622668550277569cx, 0.6658307388197702cy), \"\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.9347920910961409cx, 0.7584240166015694cy), \"C18\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.6242874004134858cx, 0.3165349821765907cy), \"\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.7790234819358453cx, 0.977236076734393cy), \"C16\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.8933875345010103cx, 0.8343680475842656cy), \"C1\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.6266846306550453cx, 0.11369998137335086cy), \"\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.49092090903644264cx, 0.18834850235034944cy), \"\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, 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      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gplot(\n",
    "    t,\n",
    "    nodesize = dsize,\n",
    "    nodelabel = labels,\n",
    "    nodelabeldist=2.5,\n",
    "    #nodelabelsize=size,\n",
    "    nodefillc=get_color.(1:Graphs.nv(t), Ref(comms.np), Ref(reverse(my_colors)))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Communities from CNM-like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((-0.38672206860250535cx, -0.17768889387898076cy), \"\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((-0.3997660817809017cx, 0.5938770124140543cy), \"C15\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((-0.34868463360484864cx, 0.6148716419461157cy), \"C14\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((0.011867643025463517cx, 0.9477292537492945cy), \"\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((-0.07921671116110394cx, 0.9323718475657117cy), \"C8\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm)), Compose.TextPrimitive{Tuple{Measures.Length{:cx, Float64}, Measures.Length{:cy, Float64}}, Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}, Tuple{Measures.AbsoluteLength, Measures.AbsoluteLength}}((-0.4198774757660292cx, 0.026130014690308956cy), \"C7\", Compose.HCenter(), Compose.VCenter(), Compose.Rotation{Tuple{Measures.Length{:w, Float64}, Measures.Length{:h, Float64}}}(0.0, (0.5w, 0.5h)), (0.0mm, 0.0mm))], Symbol(\"\"))]), List([Compose.Property{Compose.FontSizePrimitive}(Compose.FontSizePrimitive[Compose.FontSizePrimitive(2.3612040133779266mm), Compose.FontSizePrimitive(2.3612040133779266mm), Compose.FontSizePrimitive(2.795986622073579mm), Compose.FontSizePrimitive(2.2943143812709033mm), Compose.FontSizePrimitive(2.3612040133779266mm), Compose.FontSizePrimitive(3.5986622073578602mm), Compose.FontSizePrimitive(3.4983277591973247mm), Compose.FontSizePrimitive(3.4983277591973247mm), Compose.FontSizePrimitive(4.0mm), Compose.FontSizePrimitive(3.6655518394648836mm)  …  Compose.FontSizePrimitive(2.3946488294314383mm), Compose.FontSizePrimitive(2.3946488294314383mm), Compose.FontSizePrimitive(2.3277591973244154mm), Compose.FontSizePrimitive(2.3277591973244154mm), Compose.FontSizePrimitive(2.3277591973244154mm), Compose.FontSizePrimitive(2.461538461538462mm), Compose.FontSizePrimitive(2.461538461538462mm), Compose.FontSizePrimitive(2.3946488294314383mm), Compose.FontSizePrimitive(2.3946488294314383mm), Compose.FontSizePrimitive(2.3946488294314383mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[Compose.StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\"))]), List([]), List([]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\"))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gplot(\n",
    "    t, \n",
    "    nodesize = dsize,\n",
    "    nodelabel = cnm_labels,\n",
    "    nodelabeldist = 5.5,\n",
    "    nodelabelsize = dsize,\n",
    "    nodefillc = get_color.(1:Graphs.nv(t), Ref(cnm_comms.bp), Ref(my_colors))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which are the most important characters?\n",
    "Identifying truly important and influential characters in a vast narrative like GoT may not be a trivial task, as it depends on the considered level of granularity. In these cases, the main character(s) in each plotline is referred with the term fractal protagonist(s), to indicate that the answer to \"who is the protagonist\" depends on the specific plotline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Degree centrality\n",
    "Who are the characters that apper in the majority of the scenes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "degrees = Dict{Int, Int}()\n",
    "\n",
    "for v=1:nhv(hg)\n",
    "    degrees[v] = length(gethyperedges(hg, v))\n",
    "end\n",
    "\n",
    "sorted_degrees = sort(collect(degrees), by=x->x[2], rev=true);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's plot these data\n",
    "characters = Array{String, 1}()\n",
    "degrees = Array{Int, 1}()\n",
    "\n",
    "max_c = 0\n",
    "\n",
    "# we will visualize only characters appearing in at least 15 scenes\n",
    "for c in sorted_degrees\n",
    "    max_c = max_c > 15 ? break : max_c + 1\n",
    "\n",
    "    push!(characters, string(get_vertex_meta(hg, c.first)))\n",
    "    push!(degrees, c.second)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = collect(1:length(characters))\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4)) \n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "rects = ax.barh(\n",
    "    pos,\n",
    "    degrees,\n",
    "    align=\"center\",\n",
    "    tick_label=characters\n",
    ")\n",
    "\n",
    "#plt.tight_layout(.5)\n",
    "plt.tick_params(labelsize=\"large\")\n",
    "\n",
    "plt.title(\"Season 1\", pad=10, fontweight=\"semibold\", fontsize=\"x-large\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Betweenness centrality\n",
    "We investigated the importance of the characters also evaluating the betweenness centrality (BC) metric of hypergraph nodes. BC measures the centrality of a node by computing the number of times that a node acts as a bridge between the other two nodes, considering the shortest paths between them.\n",
    "\n",
    "Here, we used the concept of *s-beetwennes-centrality*. Check out the paper for more detail about this metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we evaluate betweennes value considering 1-paths, 2-paths, and 3-paths.\n",
    "betweeness = Dict{Int, Dict{Symbol, Float64}}()\n",
    "\n",
    "for s=1:3\n",
    "    A = SimpleHypergraphs.adjacency_matrix(hg; s=s)\n",
    "    G = Graphs.SimpleGraph(A)\n",
    "    bc = Graphs.betweenness_centrality(G)\n",
    "\n",
    "    for v=1:nhv(hg)\n",
    "        push!(\n",
    "            get!(betweeness, s, Dict{Symbol, Int}()),\n",
    "            get_vertex_meta(hg, v) => bc[v]\n",
    "        )\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_betweeness = Dict{Int, Any}()\n",
    "\n",
    "for s=1:3\n",
    "    d = get!(betweeness, s, Dict{Symbol, Int}())\n",
    "    d_sorted = sort(collect(d), by=x->x[2], rev=true)\n",
    "\n",
    "    push!(\n",
    "        sorted_betweeness,\n",
    "        s => d_sorted\n",
    "    )\n",
    "\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Int64, Any} with 3 entries:\n",
       "  2 => [:Tyrion_Lannister=>0.0842826, :Jon_Snow=>0.070162, :Eddard_Stark=>0.054…\n",
       "  3 => [:Jon_Snow=>0.056747, :Eddard_Stark=>0.0513413, :Tyrion_Lannister=>0.048…\n",
       "  1 => [:Arya_Stark=>0.271413, :Illyrio_Mopatis=>0.251899, :Tyrion_Lannister=>0…"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_betweeness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13-element Vector{Pair{Symbol, Vector{Float64}}}:\n",
       "       :Arya_Stark => [0.27141303430561975, 0.020811742624320874, 0.025567689757446024]\n",
       "  :Illyrio_Mopatis => [0.25189924030387845, 0.0, 0.0]\n",
       " :Tyrion_Lannister => [0.15323433877389522, 0.08428255933571452, 0.048736052575128745]\n",
       "     :Eddard_Stark => [0.11451260512166041, 0.05462031924487055, 0.051341324021137794]\n",
       "    :Catelyn_Stark => [0.11139671344052993, 0.029487862630240228, 0.033451404486765125]\n",
       "         :Jon_Snow => [0.11122931698056152, 0.07016200324650089, 0.05674696356665995]\n",
       "             :Will => [0.06384113021458084, 0.03825136612021858, 0.0]\n",
       "       :Lord_Varys => [0.06377540542298163, 0.0009193736202932523, 0.001578733585930707]\n",
       "    :Rodrik_Cassel => [0.05228209175473733, 0.023788449409135447, 0.017966399768193867]\n",
       "       :Bran_Stark => [0.042489406954219586, 0.0441890894890005, 0.029071370024299194]\n",
       "    :Theon_Greyjoy => [0.038163554220999756, 0.030162976584305612, 0.023194820979115627]\n",
       " :Robert_Baratheon => [0.01766998385978779, 0.014594012937620571, 0.014715908661845302]\n",
       "      :Sansa_Stark => [0.015381909177319377, 0.020828998970610812, 0.012916363931724326]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting top 10 characters for each s-value\n",
    "#character => (HG_degree, G_degree)\n",
    "data = Dict{Symbol, Array{Float64, 1}}()\n",
    "\n",
    "for s=1:3\n",
    "    higher_degree_characters = sorted_betweeness[s][1:10]\n",
    "\n",
    "    for elem in higher_degree_characters\n",
    "        if !haskey(data, elem.first)\n",
    "            if s==1\n",
    "                push!(\n",
    "                    get!(data, elem.first, Array{Float64, 1}()),\n",
    "                    elem.second,\n",
    "                    betweeness[2][elem.first],\n",
    "                    betweeness[3][elem.first]\n",
    "                )\n",
    "            elseif s==2\n",
    "                push!(\n",
    "                    get!(data, elem.first, Array{Float64, 1}()),\n",
    "                    betweeness[1][elem.first],\n",
    "                    elem.second,\n",
    "                    betweeness[3][elem.first]\n",
    "                )\n",
    "            else\n",
    "                push!(\n",
    "                    get!(data, elem.first, Array{Float64, 1}()),\n",
    "                    betweeness[1][elem.first],\n",
    "                    betweeness[2][elem.first],\n",
    "                    elem.second\n",
    "                )\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "end\n",
    "\n",
    "#by highest betweenes degree in the graph, s=1\n",
    "sorted_data = sort(collect(data), by=x->x[2][1], rev=true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 750x450 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = Array{String, 1}()\n",
    "s1 = Array{Float64, 1}()\n",
    "s2 = Array{Float64, 1}()\n",
    "s3 = Array{Float64, 1}()\n",
    "\n",
    "for elem in sorted_data\n",
    "    push!(labels, string(elem.first))\n",
    "    push!(s1, elem.second[1])\n",
    "    push!(s2, elem.second[2])\n",
    "    push!(s3, elem.second[3])\n",
    "end\n",
    "\n",
    "clf()\n",
    "fig = plt.figure(figsize=(7.5, 4.5))\n",
    "ax = plt.axes([0.12, 0.32, 0.85, 0.6])\n",
    "\n",
    "pos = collect(1:length(labels))# the x locations for the groups\n",
    "width = 0.3  # the width of the bars\n",
    "\n",
    "s1_rects = ax.bar(pos .- width/2, s1, width, label=L\"$1-path$\")\n",
    "s2_rects = ax.bar(pos .+ width/2, s2, width, label=L\"$2-path$\")\n",
    "s3_rects = ax.bar(pos .+ (width+width/2), s2, width, label=L\"$3-path$\")\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "plt.title(\"Season 8\", pad=10, fontweight=\"semibold\", fontsize=\"x-large\")\n",
    "\n",
    "plt.ylabel(\"Betweeness Centrality Score\", fontweight=\"semibold\", fontsize=\"x-large\", labelpad=10)\n",
    "\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "ax.set_yticks(collect(range(0, stop=(.40 > maximum(s1) ? .4 : maximum(s1)), step=0.05))) #(season == 8 ? 25 : 10)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.setp(ax.xaxis.get_majorticklabels(), rotation=65, ha=\"right\", rotation_mode=\"anchor\")\n",
    "plt.tick_params(labelsize=\"large\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Some comments\n",
    "The plot above, showing the betweenness centrality scores using (1,2,3)-paths, reveals that removing \"shallow\" relationships can bring to light a completely different insight about the structure of the network we are analyzing. Specifically, in this case, it is worth noting that:\n",
    "* Illyrio_Mopatis and Lord_Varys appear among the ten most important characters. However, they disappear when using (2,3)-paths, suggesting that these characters did not contribute in an evident way to the plot (but maybe behind the scenes);\n",
    "* Arya_Stark appear to be the most critical character when considering 1-paths. Nonetheless, her importance decreases when switching to (2,3)-paths.\n",
    "\n",
    "This example highlight how just using 2-paths, we can grasp how characters really interact in the plot and which are the guys who tie all characters together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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