{
 "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Pkg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pkg\"add PyCall Conda SimpleHypergraphs Plots GraphPlot ColorSchemes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prerequisites for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using Conda, PyCall\n",
    "#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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "using SimpleHypergraphs\n",
    "using Graphs\n",
    "using Plots\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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pth = joinpath(dirname(pathof(SimpleHypergraphs)),\"..\",\"tutorials\", \"basics\", \"data\", \"hg_seasons_all.json\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = SimpleHypergraphs.hg_load(pth; format=JSON_Format(), T=Bool, V=Symbol, E=Symbol);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "577"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "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": 9,
   "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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pth = joinpath(dirname(pathof(SimpleHypergraphs)),\"..\",\"tutorials\", \"basics\", \"data\", \"hg_seasons_min.json\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length.(h1.he2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.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": 16,
   "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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"125 characters and 286 scenes\""
      ]
     },
     "execution_count": 18,
     "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": 19,
   "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": 19,
     "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": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_vertex!(hg, 99)\n",
    "cc = get_connected_components(hg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"We found 18 communities in the hypergraph of the 1st season.\""
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length.(comms.np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5040026252111678"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"We found 14 communities in the hypergraph of the 1st season with a modularity value of 0.44905422798563704.\""
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Int64, Int64} with 3 entries:\n",
       "  93 => 1\n",
       "  1  => 12\n",
       "  19 => 1"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White_Walker\n",
      "Renly_Baratheon\n",
      "Grand_Maester_Pycelle\n",
      "Ros\n",
      "Joyeuse_Erenford\n",
      "Addam_Marbrand\n",
      "Chella\n",
      "Hodor\n",
      "Myrcella_Baratheon\n",
      "Jon_Snow\n",
      "Shae\n",
      "Gendry\n",
      "Stevron_Frey\n",
      "Lancel_Lannister\n",
      "Arya_Stark\n",
      "Wight_Wildling_Girl\n",
      "Lord_Varys\n",
      "Barristan_Selmy\n",
      "Wallen\n",
      "Shagga\n",
      "Tywin_Lannister\n",
      "Leo_Lefford\n",
      "Old_Nan\n",
      "Mycah\n",
      "Sansa_Stark\n",
      "Joffrey_Baratheon\n",
      "Gregor_Clegane\n",
      "Ghost\n",
      "Lannister_Messenger\n",
      "Armeca\n",
      "Benjen_Stark\n",
      "Three-Eyed_Raven\n",
      "Rickon_Stark\n",
      "Syrio_Forel\n",
      "Varly\n",
      "Stiv\n",
      "Mord\n",
      "King's_Landing_Baker\n",
      "Street_Urchin\n",
      "Osha\n",
      "Bronn\n",
      "Bran_Stark\n",
      "Cersei_Lannister\n",
      "Othell_Yarwyck\n",
      "Timett\n",
      "Janos_Slynt\n",
      "Yoren\n",
      "Marillion\n",
      "Tyrion_Lannister\n",
      "Jeor_Mormont\n",
      "Catspaw_Assassin\n",
      "Waymar_Royce\n",
      "Hot_Pie\n",
      "Catelyn_Stark\n",
      "Maester_Luwin\n",
      "Jaime_Lannister\n",
      "Grey_Wind\n",
      "Ryger_Rivers\n",
      "Meryn_Trant\n",
      "Vardis_Egen\n",
      "Septa_Mordane\n",
      "Nymeria\n",
      "Robin_Arryn\n",
      "Lady\n",
      "Eddard_Stark\n",
      "Robert_Baratheon\n",
      "Tobho_Mott\n",
      "Kevan_Lannister\n",
      "Sandor_Clegane\n",
      "Greatjon_Umber\n",
      "Petyr_Baelish\n",
      "Othor\n",
      "Samwell_Tarly\n",
      "Summer\n",
      "Rickard_Karstark\n",
      "Red_Keep_Stableboy\n",
      "Loras_Tyrell\n",
      "Hugh_of_the_Vale\n",
      "Robb_Stark\n",
      "Jory_Cassel\n",
      "Gared\n",
      "Royal_Steward\n",
      "Shaggydog\n",
      "Mhaegen\n",
      "Lommy_Greenhands\n",
      "Jonos_Bracken\n",
      "Theon_Greyjoy\n",
      "Will\n",
      "Lysa_Arryn\n",
      "Ilyn_Payne\n",
      "Tommen_Baratheon\n",
      "Walder_Frey\n",
      "Rodrik_Cassel\n",
      "-----\n",
      "Rast\n",
      "-----\n",
      "Pypar\n",
      "-----\n",
      "Jon_Arryn\n",
      "-----\n",
      "Maester_Aemon\n",
      "-----\n",
      "Beric_Dondarrion\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",
      "Rhaego\n",
      "Illyrio_Mopatis\n",
      "Rhaegal\n",
      "Jorah_Mormont\n",
      "Dothraki_Crone\n",
      "Mago\n",
      "-----\n",
      "Alliser_Thorne\n",
      "-----\n",
      "Jafer_Flowers\n",
      "-----\n",
      "Tomard\n",
      "-----\n",
      "Grenn\n",
      "-----\n",
      "Jaremy_Rykker\n",
      "-----\n",
      "Lannister_Scout\n",
      "-----\n",
      "Joss\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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{124, 886} undirected simple Int64 graph"
      ]
     },
     "execution_count": 28,
     "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": [
    {
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       "Compose.Context(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 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(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 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}[(0.602705809051733cx, 0.4407332545208037cy), (0.5213419813968485cx, 0.46009493797234613cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.6113420737157941cx, 0.42511473030489894cy), (0.4701922320000055cx, 0.00891400360851019cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.6049144419029375cx, 0.4455064852001715cy), (0.5759972781643345cx, 0.46672940257813106cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.507231346184708cx, 0.44996636584591715cy), (0.4665943781066834cx, 0.00967898250663591cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.5214430969185252cx, 0.4657696534364246cy), (0.5523195166120564cx, 0.47196389273227024cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.463263256545999cx, 0.009631311223663761cy), (0.40956876661767055cx, 0.45249276393146964cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.4683543775904882cx, 0.0094152009195435cy), (0.5626089604498505cx, 0.461596824050313cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.4739332210010338cx, -0.018953703146601338cy), (0.6238661757157435cx, -0.24102716239696456cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.4656367130319979cx, -0.021632992393218583cy), (0.46968460413223cx, -0.15265243441775248cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.4612530825154889cx, -0.021148391375067115cy), (0.4251369285926013cx, -0.16195195824249667cy)])  …  Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.7599128223139113cx, -0.9187653944317784cy), (0.8149106663898129cx, -0.9037158173880969cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.0900706254971732cx, -0.3049780332490236cy), (-0.06504996632595135cx, -0.2455252479391271cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.08934507089212666cx, -0.3294230445795301cy), (-0.06796513793087373cx, -0.3728269801109153cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.08231883528640475cx, -0.3138241620364725cy), (-0.05324794523036531cx, -0.30588250426860514cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.06003039600464615cx, -0.24657619701659564cy), (-0.061832180170591285cx, -0.37144501183291945cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.05618191331628484cx, -0.24607120002925084cy), (-0.04393723455272227cx, -0.28940665043489644cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.058602449416961785cx, -0.3718869762820801cy), (-0.043706315451921184cx, -0.31533761768436186cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(-0.1377332918222685cx, -0.4838735260993645cy), (-0.11897219892266449cx, -0.5403400224804414cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.2358756501063144cx, -0.10989680469800762cy), (0.2483681818262422cx, -0.09125709562937893cy)]), Compose.LinePrimitive{Tuple{Measures.Measure, Measures.Measure}}(Tuple{Measures.Measure, Measures.Measure}[(0.9550891516073668cx, -0.8270915699271857cy), (0.9661490214885836cx, -0.7839017540008661cy)])], 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(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 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(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 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(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 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}((0.615598448492636cx, 0.43766526705137876cy), 0.005301059499619695w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.5084493419559455cx, 0.46316292544177107cy), 0.005301059499619695w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.46515210549371666cx, -0.005947538487887982cy), 0.006277175271504342w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.4080188259739692cx, 0.46527636548903484cy), 0.005150887842406672w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.5653132715746361cx, 0.4745706207269238cy), 0.005301059499619695w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.6351681856270752cx, -0.2577671542304282cy), 0.008079235158060612w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.4702909432291704cx, -0.17227801430408718cy), 0.007853977672241077w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.42025848596673265cx, -0.18097120718985238cy), 0.007853977672241077w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.461513636834463cx, -0.44685808453814313cy), 0.008980265101338746w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.32659734154935216cx, -0.35391423064646976cy), 0.008229406815273633w)  …  Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((-0.062026096587556645cx, -0.38488397617590486cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((-0.04028266828132632cx, -0.3023406177905371cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.6744031816944489cx, -0.9897324228808012cy), 0.005225973671013184w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((-0.14185271593055693cx, -0.47147502417052967cy), 0.005225973671013184w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((-0.11485277481437606cx, -0.5527385244092763cy), 0.005225973671013184w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.22818389201199563cx, -0.12137343219983476cy), 0.005526316985439229w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.256059939920561cx, -0.0797804681275518cy), 0.005526316985439229w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.9517549819746252cx, -0.8401118110558632cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.9694831911213253cx, -0.7708815128721886cy), 0.0053761453282262055w), Compose.CirclePrimitive{Tuple{Measures.Measure, Measures.Measure}, Measures.Measure}((0.06501547156140508cx, -0.5517378592464296cy), 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(BBox{l,t,r,b,w,h = 0.0w,0.0h, 1.0w,1.0h, 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}}((0.6390260429904764cx, 0.41423767255353844cy), \"\", 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}}((0.5318769364537859cx, 0.43973533094393075cy), \"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}}((0.49289356300107434cx, -0.03368899599524565cy), \"\", 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}}((0.4307827492395761cx, 0.44251244222342795cy), \"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}}((0.5887408660724764cx, 0.4511430262290835cy), \"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}}((0.6708736979212342cx, -0.2934726665245871cy), \"\", 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}}((0.5050009486749791cx, -0.20698801974989595cy), \"\", 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}}((0.4549684914125414cx, -0.21568121263566115cy), \"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}}((0.5012011765220226cx, -0.4865456242257027cy), \"\", 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}}((0.3629665250757445cx, -0.3902834141728621cy), \"\", 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}}((-0.038266666473599595cx, -0.4086434062898619cy), \"\", 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}}((-0.01652323816736927cx, -0.32610004790449415cy), \"\", 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}}((0.6974989405761726cx, -1.0128281817625249cy), \"\", 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}}((-0.11875695704883332cx, -0.49457078305225327cy), \"\", 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}}((-0.09175701593265245cx, -0.575834283291cy), \"\", 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)), 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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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      ]
     },
     "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": {
      "text/plain": [
       "16-element Vector{Int64}:\n",
       " 70\n",
       " 44\n",
       " 41\n",
       " 38\n",
       " 36\n",
       " 32\n",
       " 29\n",
       " 26\n",
       " 26\n",
       " 24\n",
       " 24\n",
       " 22\n",
       " 22\n",
       " 22\n",
       " 21\n",
       " 18"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "</svg>\n"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = collect(1:length(characters))\n",
    "bar(degrees, orientation=:h, yticks=(pos, characters), yflip=false, bar_spacing=2.5,ylim=(0, length(pos)+1),label=nothing)\n",
    "# Set plot attributes\n",
    "title!(\"Season 1\")\n",
    "ylabel!(\"Characters\")"
   ]
  },
  {
   "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": 39,
   "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": 40,
   "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": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_betweeness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "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": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import StatsPlots\n",
    "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",
    "\n",
    "group_pos = collect(1:length(labels))# the x locations for the groups\n",
    "width = 0.3  # the width of the bars\n",
    "\n",
    "\n",
    "StatsPlots.groupedbar(group_pos,\n",
    "           [s1 s2 s3],\n",
    "           xlabel=\"Groups\",\n",
    "           ylabel=\"Betweenness Centrality Score\",\n",
    "           xticks=(group_pos, labels),\n",
    "           label=[\"1-path\" \"2-path\" \"3-path\"],\n",
    "           bar_width=0.9,\n",
    "           legend=:topright,\n",
    "           rotation=65)\n",
    "title!(\"Season 8\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": 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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