day_16_analysis.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d18c815",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e8c36c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for visualization\n",
    "from IPython.display import SVG \n",
    "from sknetwork.visualization import svg_graph \n",
    "from sknetwork.data import Bunch \n",
    "from sknetwork.ranking import PageRank \n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "def draw_graph(G, show_names=False, node_size=1, font_size=10, edge_width=0.5):\n",
    "\n",
    "    adjacency = nx.to_scipy_sparse_array(G, nodelist=None, dtype=None, weight='weight', format='csr')\n",
    "    adjacency = csr_matrix(adjacency) # fix to weird sknetwork-csr issue; comment out to troubleshoot\n",
    "    \n",
    "    names = np.array(list(G.nodes))\n",
    "    \n",
    "    graph = Bunch()\n",
    "    graph.adjacency = adjacency\n",
    "    graph.names = np.array(names)\n",
    "    \n",
    "    pagerank = PageRank()\n",
    "    scores = pagerank.fit_transform(adjacency)\n",
    "\n",
    "    if show_names:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, font_size=font_size, node_size=node_size, names=graph.names, width=700, height=500, scores=scores, edge_width=edge_width)\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, node_size=node_size, width=700, height=500, scores = scores, edge_width=edge_width)\n",
    "    \n",
    "    return SVG(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e7e69f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B.B. King</td>\n",
       "      <td>NEGAPHONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B.B. King</td>\n",
       "      <td>WeeBay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B.B. King</td>\n",
       "      <td>Big Choze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B.B. King</td>\n",
       "      <td>U2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B.B. King</td>\n",
       "      <td>Lil Wayne</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      source     target\n",
       "0  B.B. King  NEGAPHONE\n",
       "1  B.B. King     WeeBay\n",
       "2  B.B. King  Big Choze\n",
       "3  B.B. King         U2\n",
       "4  B.B. King  Lil Wayne"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = r'data/spotify_bbking_edgelist.csv'\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5296a3e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.from_pandas_edgelist(df)\n",
    "\n",
    "# drop the seed nodes; we will have proper names from pages found\n",
    "seed_nodes = ['network science', 'social network analysis', 'graph theory', 'causal inference']\n",
    "G.remove_nodes_from(seed_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7936819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 87 nodes and 88 edges\n"
     ]
    }
   ],
   "source": [
    "print(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c3b2b9ba",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is network analysis network without labels\n",
    "\n",
    "draw_graph(G, node_size=3, show_names=True, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10aa2232",
   "metadata": {},
   "source": [
    "# Egocentric Network Analysis\n",
    "\n",
    "As the above visualization shows, this is a complex network. For knowledge discovery, we should use Egocentric Network Analysis to \"zoom in\" on nodes of interest. Any node can be looked at, but I am going to programmatically do this, in this notebook, showing only the top twenty nodes, according to page rank."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dcf1d59",
   "metadata": {},
   "source": [
    "# PageRank - Identify Nodes of Interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42fbb411",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>page_rank</th>\n",
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      "text/plain": [
       "              page_rank\n",
       "Degree         0.051595\n",
       "WeeBay         0.050546\n",
       "tephe          0.049145\n",
       "Conton Candy   0.049145\n",
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       "Webbie         0.022739"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "page_rank = nx.pagerank(G)\n",
    "\n",
    "pr_df = pd.DataFrame().from_dict(page_rank, orient='index')\n",
    "pr_df.columns = ['page_rank']\n",
    "pr_df.sort_values('page_rank', ascending=False, inplace=True)\n",
    "pr_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71a59549",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Important Artists by PageRank'\n",
    "\n",
    "pr_df.head(20).plot.barh(figsize=(12,8), title=title).invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3565f936",
   "metadata": {},
   "source": [
    "# Egocentric Network Analysis\n",
    "Next, let's look at the Ego Graphs for each of the top ten characters found by PageRank. Before visualizing and inspecting the Ego Graphs, let's create a Python list so that we can more easily look through them without explicitely typing their names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d823af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_nodes = pr_df.head(20).index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "662a98f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Degree'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_nodes[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecf99c7",
   "metadata": {},
   "source": [
    "Now we are all set to start looking through them, in order of importance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290bcf1a",
   "metadata": {},
   "source": [
    "## Top Node Ego Networks\n",
    "Unlike last time, I am not going to drop the center node or write about findings. This is purely discovery. Remember two things:\n",
    "- The center node is known as the 'ego' node. It is the node being looked up.\n",
    "- The other nodes are known as 'alter' nodes. They are connected to the ego node, and sometimes with each other.\n",
    "\n",
    "Pay close attention to the alter nodes that link to other alter nodes. These are interesting relationships, and you can see subcommunities in the ego network. Think about what you are seeing. What does this tell you?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "863ca7a1",
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"726\" y=\"337\" font-size=\"10\">Kanye West</text><text text-anchor=\"start\" x=\"26\" y=\"198\" font-size=\"10\">Arno Coleridge</text><text text-anchor=\"start\" x=\"34\" y=\"352\" font-size=\"10\">Cola</text><text text-anchor=\"start\" x=\"388\" y=\"520\" font-size=\"10\">EST Gee</text><text text-anchor=\"start\" x=\"718\" y=\"180\" font-size=\"10\">$uicideboy$</text><text text-anchor=\"start\" x=\"149\" y=\"70\" font-size=\"10\">MadeByVL</text><text text-anchor=\"start\" x=\"362\" y=\"20\" font-size=\"10\">93FEETOFSMOKE</text><text text-anchor=\"start\" x=\"599\" y=\"468\" font-size=\"10\">Taylor McFerrin</text><text text-anchor=\"start\" x=\"174\" y=\"474\" font-size=\"10\">JEY</text><text text-anchor=\"start\" x=\"583\" y=\"54\" font-size=\"10\">Big Choze</text><text text-anchor=\"start\" x=\"379\" y=\"267\" font-size=\"10\">Degree</text></svg>"
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[0]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32fcd7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['$uicideboy$',\n",
       " '93FEETOFSMOKE',\n",
       " 'Arno Coleridge',\n",
       " 'Big Choze',\n",
       " 'Cola',\n",
       " 'Degree',\n",
       " 'EST Gee',\n",
       " 'JEY',\n",
       " 'Kanye West',\n",
       " 'MadeByVL',\n",
       " 'Taylor McFerrin']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can look at the nodes in the ego network this way, to find interesting topics\n",
    "\n",
    "nodes = sorted(G_ego.nodes)\n",
    "nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "025314b1",
   "metadata": {},
   "outputs": [
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "ego = top_nodes[1]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62f53797",
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "ego = top_nodes[2]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c56a973",
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   "outputs": [
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   ],
   "source": [
    "ego = top_nodes[3]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7230b1b1",
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   "outputs": [
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[4]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0260ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ego = top_nodes[5]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90dda4ba",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "ego = top_nodes[6]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1322b19e",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "ego = top_nodes[7]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dd7963b3",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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     },
     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "ego = top_nodes[8]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6cf977b9",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 21,
     "metadata": {},
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   "source": [
    "ego = top_nodes[9]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ef7164d7",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 22,
     "metadata": {},
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   ],
   "source": [
    "ego = top_nodes[10]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4eb13cf1",
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"26\" y=\"69\" font-size=\"10\">Alex Nocera</text><text text-anchor=\"start\" x=\"726\" y=\"462\" font-size=\"10\">Alexa Cappelli</text><text text-anchor=\"start\" x=\"373\" y=\"268\" font-size=\"10\">Alexnocap</text><text text-anchor=\"start\" x=\"645\" y=\"20\" font-size=\"10\">Alex Campos</text><text text-anchor=\"start\" x=\"95\" y=\"520\" font-size=\"10\">WeeBay</text></svg>"
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     },
     "execution_count": 23,
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   ],
   "source": [
    "ego = top_nodes[11]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ee577df8",
   "metadata": {},
   "outputs": [
    {
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       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 367 274 720 268\"/>\n",
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       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 367 274 374 520\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 367 274 365 20\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 720 268 367 274\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 20 273 367 274\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 374 520 367 274\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 365 20 367 274\"/>\n",
       "<circle cx=\"367\" cy=\"274\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"720\" cy=\"268\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"273\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"374\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"365\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"373\" y=\"274\" font-size=\"10\">Abdeelgha4</text><text text-anchor=\"start\" x=\"726\" y=\"268\" font-size=\"10\">Abdallah Adel</text><text text-anchor=\"start\" x=\"26\" y=\"273\" font-size=\"10\">Abdel Halim Hafez</text><text text-anchor=\"start\" x=\"380\" y=\"520\" font-size=\"10\">NEGAPHONE</text><text text-anchor=\"start\" x=\"371\" y=\"20\" font-size=\"10\">Abdelli</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[12]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac1d528b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"790.0\" height=\"540\">\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 365 270 376 20\"/>\n",
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       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 376 20 365 270\"/>\n",
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       "<circle cx=\"365\" cy=\"270\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
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       "<circle cx=\"20\" cy=\"265\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"361\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"371\" y=\"270\" font-size=\"10\">Lil Wayne</text><text text-anchor=\"start\" x=\"382\" y=\"20\" font-size=\"10\">Jay Sean</text><text text-anchor=\"start\" x=\"726\" y=\"275\" font-size=\"10\">Drake</text><text text-anchor=\"start\" x=\"26\" y=\"265\" font-size=\"10\">B.B. King</text><text text-anchor=\"start\" x=\"367\" y=\"520\" font-size=\"10\">Tyga</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[13]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e670aa2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"760.0\" height=\"540\">\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 379 190 720 20\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 379 190 391 520\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 379 190 20 42\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 720 20 379 190\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 391 520 379 190\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 20 42 379 190\"/>\n",
       "<circle cx=\"379\" cy=\"190\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"720\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"391\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"42\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"385\" y=\"190\" font-size=\"10\">Kendrick Lamar</text><text text-anchor=\"start\" x=\"726\" y=\"20\" font-size=\"10\">U2</text><text text-anchor=\"start\" x=\"397\" y=\"520\" font-size=\"10\">Baby Keem</text><text text-anchor=\"start\" x=\"26\" y=\"42\" font-size=\"10\">Taylor Swift</text></svg>"
      ],
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       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ego = top_nodes[14]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "646511f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"870.0\" height=\"540\">\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 555 520 442 233\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 442 233 555 520\"/>\n",
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       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 442 233 720 20\"/>\n",
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       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 720 20 442 233\"/>\n",
       "<circle cx=\"555\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"442\" cy=\"233\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"169\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"720\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"561\" y=\"520\" font-size=\"10\">Justine Skye</text><text text-anchor=\"start\" x=\"448\" y=\"233\" font-size=\"10\">Tyga</text><text text-anchor=\"start\" x=\"26\" y=\"169\" font-size=\"10\">Lil Wayne</text><text text-anchor=\"start\" x=\"726\" y=\"20\" font-size=\"10\">Travis Porter</text></svg>"
      ],
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       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ego = top_nodes[15]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aca75f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"850.0\" height=\"540\">\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 720 321 288 280\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 118 20 288 280\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 20 520 288 280\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 288 280 720 321\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 288 280 118 20\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 288 280 20 520\"/>\n",
       "<circle cx=\"720\" cy=\"321\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"118\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"288\" cy=\"280\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"726\" y=\"321\" font-size=\"10\">Jack Harlow</text><text text-anchor=\"start\" x=\"124\" y=\"20\" font-size=\"10\">Kanye West</text><text text-anchor=\"start\" x=\"26\" y=\"520\" font-size=\"10\">Big Choze</text><text text-anchor=\"start\" x=\"294\" y=\"280\" font-size=\"10\">Big Sean</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[16]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "563015ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"780.0\" height=\"540\">\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 439 241 720 20\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 439 241 20 183\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 439 241 576 520\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 720 20 439 241\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 20 183 439 241\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 576 520 439 241\"/>\n",
       "<circle cx=\"439\" cy=\"241\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"720\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"183\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"576\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"445\" y=\"241\" font-size=\"10\">Kanye West</text><text text-anchor=\"start\" x=\"726\" y=\"20\" font-size=\"10\">Mons</text><text text-anchor=\"start\" x=\"26\" y=\"183\" font-size=\"10\">Big Sean</text><text text-anchor=\"start\" x=\"582\" y=\"520\" font-size=\"10\">Degree</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[17]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f2254f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"830.0\" height=\"540\">\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 720 20 359 259\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 359 259 720 20\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 359 259 20 520\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 20 520 359 259\"/>\n",
       "<circle cx=\"720\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"359\" cy=\"259\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"726\" y=\"20\" font-size=\"10\">Lil Wayne</text><text text-anchor=\"start\" x=\"365\" y=\"259\" font-size=\"10\">Jay Sean</text><text text-anchor=\"start\" x=\"26\" y=\"520\" font-size=\"10\">Rishi Rich</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[18]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26365521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"860.0\" height=\"540\">\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 367 261 20 20\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 367 261 720 520\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 20 20 367 261\"/>\n",
       "<path stroke-width=\"0.3\" stroke=\"gray\" d=\"M 720 520 367 261\"/>\n",
       "<circle cx=\"367\" cy=\"261\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"720\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"373\" y=\"261\" font-size=\"10\">Drake</text><text text-anchor=\"start\" x=\"26\" y=\"20\" font-size=\"10\">Lil Wayne</text><text text-anchor=\"start\" x=\"726\" y=\"520\" font-size=\"10\">Travis Scott</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[19]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41a5cc6",
   "metadata": {},
   "source": [
    "# Shortest Paths\n",
    "\n",
    "In a collaboration network, shortest paths can be interesting, to know how to get from one person to another, or one artist to another.\n",
    "\n",
    "What in the world did Lil Wayne do with B.B. King? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "47ba1a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B.B. King', 'Lil Wayne']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.shortest_path(G, 'B.B. King', 'Lil Wayne')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "10c41904",
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "ego = 'B.B. King'\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego, 2)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=4, edge_width=0.3)"
   ]
  },
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   "cell_type": "markdown",
   "id": "87c48f30",
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   "source": [
    "# Next Steps\n",
    "\n",
    "This is a very useful tool for looking at collaboration networks in music. Some musicians do not collaborate with other musicians, so the spotify crawler returns smaller networks than the wikipedia crawler. But these are direct relationships from differerent musical collaborations, searched via the Spotify API.\n",
    "\n",
    "Next steps are to troubleshoot this crawler. There are likely bugs, in the current state. "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "9c1f8b70",
   "metadata": {},
   "outputs": [],
   "source": []
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