01_graph_types.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dramatic-passage",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "compound-england",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_graph_info(graph):\n",
    "    print(\"Number of nodes:\", graph.number_of_nodes())\n",
    "    print(\"Number of edges:\", graph.number_of_edges())\n",
    "    print(\"Available nodes:\", list(graph.nodes))\n",
    "    print(\"Available edges:\", list(graph.edges))\n",
    "    if type(graph) == nx.classes.digraph.DiGraph:\n",
    "        print(\"Connected components:\", \n",
    "              list(nx.weakly_connected_components(directed_graph)))\n",
    "    else:\n",
    "        print(\"Connected components:\", list(nx.connected_components(graph)))\n",
    "    print(\"Node degree:\", dict(undirected_graph.degree()))\n",
    "    \n",
    "def draw_graph(graph, figsize=(12,8), title=\"\"):\n",
    "    # reproducible chart\n",
    "    np.random.seed(20)\n",
    "    \n",
    "    # graph type checking\n",
    "    graph_type = \"\"\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    for n1, n2, attribute in graph.edges.data():\n",
    "        if list(attribute.keys()) != []:\n",
    "            graph_type = list(attribute.keys())[0]\n",
    "        break\n",
    "    for _, node_attribute in bipartite_graph.nodes(data=True):\n",
    "        if 'bipartite' in node_attribute.keys():\n",
    "            graph_type = \"bipartite\"\n",
    "        break\n",
    "    \n",
    "    if graph_type == \"sign\":\n",
    "        edge_color_dict = {1: \"green\", -1: \"red\"}\n",
    "        edge_colors = [edge_color_dict[sign] for _, _, sign in graph.edges(data=\"sign\")]\n",
    "        nx.draw(graph, with_labels=True, edge_color=edge_colors, node_size=400, font_color='white')\n",
    "    elif graph_type == \"weight\":\n",
    "        pos = nx.spring_layout(graph)\n",
    "        nx.draw(graph, pos, with_labels=True, font_color='white')\n",
    "        edge_labels = dict([((n1, n2), weight) for n1, n2, weight in graph.edges(data=\"weight\")])\n",
    "        nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels)\n",
    "    elif graph_type == \"relation\":\n",
    "        # create color mapping\n",
    "        edge_color_dict = {0:'blue', 1:'red', 2:'green', 3:'orange', 4:'black'}\n",
    "        relation_values = nx.get_edge_attributes(multigraph,'relation').values()\n",
    "        unique_relations = list(set(relation_values))\n",
    "        relation_color_dict = dict(zip(unique_relations , \n",
    "                                       [0] * len(unique_relations)))\n",
    "        relation_index_dict = dict(zip(unique_relations , \n",
    "                                       [i for i in range(len(unique_relations))]))\n",
    "        for i in range(len(unique_relations)):\n",
    "            relation_color_dict[unique_relations[i]] = edge_color_dict[i]\n",
    "\n",
    "        # draw nodes and node labels\n",
    "        pos = nx.spring_layout(multigraph)\n",
    "        nx.draw_networkx_nodes(multigraph, pos)\n",
    "        nx.draw_networkx_labels(multigraph, pos, font_color='w')\n",
    "\n",
    "        # draw edges with connection style of arc3 and specify the rad arguments\n",
    "        for e in multigraph.edges(data=\"relation\"):\n",
    "            ax.annotate(\"\",\n",
    "                        xy=pos[e[0]], \n",
    "                        xycoords='data',\n",
    "                        xytext=pos[e[1]], \n",
    "                        textcoords='data',\n",
    "                        arrowprops=dict(\n",
    "                            arrowstyle=\"-\", \n",
    "                            color=relation_color_dict[e[2]],\n",
    "                            shrinkA=5, shrinkB=5,\n",
    "                            connectionstyle=\"arc3,rad=rrr\".replace('rrr',\n",
    "                                str(round(0.3*relation_index_dict[e[2]],2))),\n",
    "                        ),\n",
    "            )\n",
    "        plt.axis('off')\n",
    "    elif graph_type == \"bipartite\":\n",
    "        # put L_set nodes at x=1 and R_set nodes at x=2\n",
    "        L_set, R_set = bipartite.sets(graph)\n",
    "        pos = dict()\n",
    "        pos.update((n, (1, i)) for i, n in enumerate(L_set)) \n",
    "        pos.update((n, (2, i)) for i, n in enumerate(R_set))\n",
    "\n",
    "        # create node color list for coloring\n",
    "        node_colors = []\n",
    "        for node, set_number in graph.nodes(data=\"bipartite\"):\n",
    "            if set_number == 1:\n",
    "                node_colors.append(\"green\")\n",
    "            else:\n",
    "                node_colors.append(\"blue\")\n",
    "\n",
    "        # draw the bipartite graph\n",
    "        nx.draw(graph, pos=pos, with_labels=True,\n",
    "                node_size=400, node_color=node_colors, font_color='white')\n",
    "    else:\n",
    "        nx.draw(graph, with_labels=True, font_color='white')\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    \n",
    "def write_edgelist(graph, filename, sep=\",\", data_attributes=False):\n",
    "    try:\n",
    "        nx.write_edgelist(graph, filename, delimiter=sep, data=data_attributes)\n",
    "        print(filename, \"is written sucessfully!\")\n",
    "    except:\n",
    "        print(filename, \"failed to write!\")\n",
    "        \n",
    "def write_graph_to_pickle(graph, filename=\"temp.pkl\"):\n",
    "    import pickle\n",
    "    pickle.dump(graph, open(filename, 'wb'))\n",
    "    print(filename, \"is save!!!\")\n",
    "    \n",
    "def read_graph_from_pickle(filename):\n",
    "    import os, pickle\n",
    "    if os.path.isfile(filename):\n",
    "        print(\"graph is loaded and returned\")\n",
    "        return pickle.load(open(filename, mode='rb'))\n",
    "    else:\n",
    "        \"Input file doesn't exist!!!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-negotiation",
   "metadata": {},
   "source": [
    "## 1. Undirected graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decimal-filling",
   "metadata": {},
   "source": [
    "### 1.1 Create graph using `nx.Graph()` and `add_edge()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "accompanied-mention",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'C', 'D', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'C': 1, 'D': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create graph by adding edges\n",
    "undirected_graph = nx.Graph()\n",
    "undirected_graph.add_edge('A','B')\n",
    "undirected_graph.add_edge('B','C')\n",
    "undirected_graph.add_edge('D','A')\n",
    "undirected_graph.add_edge('E','F')\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(undirected_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "former-midwest",
   "metadata": {},
   "source": [
    "### 1.2 Create graph using `nx.Graph()` class constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "collect-copper",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'D', 'C', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 2.05 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create graph from edge list\n",
    "edgelist = [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
    "undirected_graph = nx.Graph(edgelist)\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(undirected_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agreed-dodge",
   "metadata": {},
   "source": [
    "### 1.3 Create graph using `nx.from_pandas_edgelist()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sonic-illustration",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'D', 'C', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 178 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# create graph from pandas dataframe\n",
    "relationships = pd.DataFrame({'from': ['A', 'A', 'B', 'E'], \n",
    "                              'to':   ['B', 'D', 'C', 'F']})\n",
    "G = nx.from_pandas_edgelist(relationships, 'from', 'to', create_using=nx.Graph())\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(undirected_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "unauthorized-monitor",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the undirected graph\n",
    "draw_graph(undirected_graph, title=\"Undirected graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uniform-jordan",
   "metadata": {},
   "source": [
    "### 1.4 Export edge list to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "broadband-vermont",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edgelist_output/undirected_edgelist.csv is written sucessfully!\n"
     ]
    }
   ],
   "source": [
    "write_edgelist(undirected_graph, \"edgelist_output/undirected_edgelist.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weekly-liechtenstein",
   "metadata": {},
   "source": [
    "## 2. Directed graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medieval-ottawa",
   "metadata": {},
   "source": [
    "### 2.1 Create directed graph using `nx.DiGraph()` and `add_edge()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "virgin-drama",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'C', 'D', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('B', 'C'), ('D', 'A'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create graph by adding edges\n",
    "directed_graph = nx.DiGraph()\n",
    "directed_graph.add_edge('A','B')\n",
    "directed_graph.add_edge('B','C')\n",
    "directed_graph.add_edge('D','A')\n",
    "directed_graph.add_edge('E','F')\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(directed_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "monthly-stylus",
   "metadata": {},
   "source": [
    "### 2.2 Create graph using `nx.DiGraph()` class constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "moving-fairy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'D', 'C', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create graph from edge list\n",
    "edgelist = [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
    "directed_graph = nx.DiGraph(edgelist)\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(directed_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seventh-polyester",
   "metadata": {},
   "source": [
    "### 2.3 Create graph using `nx.from_pandas_edgelist()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "complimentary-eligibility",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'D', 'C', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 15 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# create graph from pandas dataframe\n",
    "relationships = pd.DataFrame({'from': ['A', 'A', 'B', 'E'], \n",
    "                              'to':   ['B', 'D', 'C', 'F']})\n",
    "G = nx.from_pandas_edgelist(relationships, 'from', 'to', create_using=nx.DiGraph())\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(directed_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "neither-sauce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the directed graph\n",
    "draw_graph(directed_graph, title=\"Directed graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impossible-cycling",
   "metadata": {},
   "source": [
    "### 2.4 Export edge list to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "textile-morris",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edgelist_output/directed_edgelist.csv is written sucessfully!\n"
     ]
    }
   ],
   "source": [
    "write_edgelist(directed_graph, \"edgelist_output/directed_edgelist.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "located-biology",
   "metadata": {},
   "source": [
    "## 3. Signed graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "industrial-president",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'C', 'D', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create signed graph\n",
    "signed_graph = nx.Graph()\n",
    "signed_graph.add_edge('A','B', sign= 1)\n",
    "signed_graph.add_edge('B','C', sign= -1)\n",
    "signed_graph.add_edge('D','A', sign= 1)\n",
    "signed_graph.add_edge('E','F', sign= -1)\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(signed_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dense-devices",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 93.7 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "draw_graph(signed_graph, title=\"Signed Graph with sign of -1 and 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "korean-fleece",
   "metadata": {},
   "source": [
    "### 3.2 Export edge list to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "unauthorized-german",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edgelist_output/signed_edgelist.csv is written sucessfully!\n"
     ]
    }
   ],
   "source": [
    "write_edgelist(signed_graph, \"edgelist_output/signed_edgelist.csv\", data_attributes=[\"sign\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-stomach",
   "metadata": {},
   "source": [
    "## 4. Weighted graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "statewide-rotation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 6\n",
      "Number of edges: 4\n",
      "Available nodes: ['A', 'B', 'C', 'D', 'E', 'F']\n",
      "Available edges: [('A', 'B'), ('A', 'D'), ('B', 'C'), ('E', 'F')]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}, {'F', 'E'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 15 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# create signed graph\n",
    "weighted_graph = nx.Graph()\n",
    "weighted_graph.add_edge('A','B', weight= 6)\n",
    "weighted_graph.add_edge('B','C', weight= 13)\n",
    "weighted_graph.add_edge('D','A', weight= 2)\n",
    "weighted_graph.add_edge('E','F', weight= 20)\n",
    "\n",
    "# display graph info\n",
    "get_graph_info(weighted_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "pressed-publisher",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the directed graph\n",
    "draw_graph(weighted_graph, title=\"Weighted graph: stronger weight means closer tie\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ancient-postage",
   "metadata": {},
   "source": [
    "### 4.2 Export edge list to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "rotary-dairy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edgelist_output/weighted_edgelist.csv is written sucessfully!\n"
     ]
    }
   ],
   "source": [
    "write_edgelist(weighted_graph, \"edgelist_output/weighted_edgelist.csv\", data_attributes=[\"weight\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recreational-windsor",
   "metadata": {},
   "source": [
    "## 5. Multigraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "promotional-intake",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 4\n",
      "Number of edges: 8\n",
      "Available nodes: ['A', 'B', 'C', 'D']\n",
      "Available edges: [('A', 'B', 0), ('A', 'B', 1), ('A', 'B', 2), ('A', 'C', 0), ('B', 'C', 0), ('B', 'C', 1), ('B', 'C', 2), ('C', 'D', 0)]\n",
      "Connected components: [{'C', 'B', 'A', 'D'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n"
     ]
    }
   ],
   "source": [
    "multigraph = nx.MultiGraph()\n",
    "multigraph.add_edge('A','B', relation= 'friend')\n",
    "multigraph.add_edge('A','B', relation= 'neighbor')\n",
    "multigraph.add_edge('A','B', relation= 'coworker')\n",
    "multigraph.add_edge('B','C', relation= 'neighbor')\n",
    "multigraph.add_edge('B','C', relation= 'family')\n",
    "multigraph.add_edge('C','B', relation= 'coworker')\n",
    "multigraph.add_edge('C','A', relation= 'coworker')\n",
    "multigraph.add_edge('D','C', relation= 'coworker')\n",
    "get_graph_info(multigraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "supreme-carroll",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['relation'])\n",
      "relation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the directed graph\n",
    "draw_graph(multigraph, title=\"Multigraph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stainless-constraint",
   "metadata": {},
   "source": [
    "### 5.1 Export edge list to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "hollywood-stomach",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edgelist_output/multigraph_edgelist.csv is written sucessfully!\n"
     ]
    }
   ],
   "source": [
    "write_edgelist(multigraph, \"edgelist_output/multigraph_edgelist.csv\", data_attributes=[\"relation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afraid-castle",
   "metadata": {},
   "source": [
    "## 6. Bipartite Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cultural-berlin",
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms import bipartite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "sized-mills",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 8\n",
      "Number of edges: 8\n",
      "Available nodes: [1, 2, 3, 4, 'a', 'b', 'c', 'd']\n",
      "Available edges: [(1, 'a'), (1, 'b'), (1, 'c'), (2, 'a'), (2, 'b'), (2, 'd'), (3, 'c'), (4, 'a')]\n",
      "Connected components: [{1, 2, 3, 4, 'b', 'c', 'd', 'a'}]\n",
      "Node degree: {'A': 2, 'B': 2, 'D': 1, 'C': 1, 'E': 1, 'F': 1}\n",
      "Wall time: 995 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "bipartite_graph = nx.Graph()\n",
    "bipartite_graph.add_nodes_from([1, 2, 3, 4], bipartite=0)\n",
    "bipartite_graph.add_nodes_from([\"a\", \"b\", \"c\", \"d\"], bipartite=1)\n",
    "bipartite_graph.add_edges_from([(1, \"a\"), (1, \"b\"), (1, \"c\"), (2, \"a\"), \n",
    "                  (2, \"b\"), (2, \"d\"), (3, \"c\"), (4, \"a\")])\n",
    "get_graph_info(bipartite_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "sustained-mainland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 119 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# visualize the graph\n",
    "draw_graph(bipartite_graph, title=\"Bipartite graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "driving-salem",
   "metadata": {},
   "source": [
    "### 6.1 Export edge list to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "practical-burner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edgelist_output/bipartite_edgelist.csv is written sucessfully!\n"
     ]
    }
   ],
   "source": [
    "write_edgelist(bipartite_graph, \"edgelist_output/bipartite_edgelist.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frequent-behavior",
   "metadata": {},
   "source": [
    "### 6.2 Export graph object to pickle object file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "placed-wells",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bipartite.pkl is save!!!\n"
     ]
    }
   ],
   "source": [
    "write_graph_to_pickle(bipartite_graph, filename=\"bipartite.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "earned-interval",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph is loaded and returned\n"
     ]
    }
   ],
   "source": [
    "bipartite_graph_loaded = read_graph_from_pickle(\"bipartite.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "oriental-costume",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graph(bipartite_graph_loaded, title=\"Bipartite graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-theorem",
   "metadata": {},
   "source": [
    "## 7. Projected Graph from Bipartite Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "collect-separation",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_projected_graph(graph, left_or_right=\"L\"):\n",
    "    L_set, R_set = bipartite.sets(graph)\n",
    "    if left_or_right == \"L\":\n",
    "        return bipartite.projected_graph(graph, L_set)\n",
    "    else:\n",
    "        return bipartite.projected_graph(graph, R_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "prescription-anxiety",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_projected_graph(projected_graph, figsize=(12,8), title=\"\"):\n",
    "    np.random.seed(20)\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    nx.draw_circular(projected_graph, with_labels = True, \n",
    "                     node_color=\"blue\", node_size=400, \n",
    "                     font_color=\"white\", font_size=15)\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "substantial-influence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "projected_graph = get_projected_graph(bipartite_graph)\n",
    "draw_projected_graph(projected_graph, title=\"L-projected graph from bipartite graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lonely-improvement",
   "metadata": {},
   "source": [
    "#### Node 2 and node 3 don't have common neighbor in the R set of the bipartite graph. The reasoning applies to node 3 and node 4. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blessed-cowboy",
   "metadata": {},
   "source": [
    "### 7.1 Export edge list to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "frequent-sponsorship",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edgelist_output/l_projected_graph_edgelist.csv is written sucessfully!\n"
     ]
    }
   ],
   "source": [
    "write_edgelist(projected_graph, \"edgelist_output/l_projected_graph_edgelist.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}