graph/00_graph_intro1.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "###### Graph Init ######\n"
     ]
    }
   ],
   "source": [
    "print(\"###### Graph Init ######\")\n",
    "G = nx.Graph() # 방향성 없는 그래프\n",
    "DiGraph = nx.DiGraph() # 방향성 있는 그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "###### Add nodes to graph ######\n"
     ]
    }
   ],
   "source": [
    "print(\"###### Add nodes to graph ######\")\n",
    "for i in range(1, 11):\n",
    "    G.add_node(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Num of nodes in G:  10\nGraph:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "print(\"Num of nodes in G: \", G.number_of_nodes())\n",
    "print(\"Graph: \", G.nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "###### Add edges to graph ######\nGraph:  [(1, 2), (1, 3), (1, 4), (2, 7), (2, 5), (5, 8), (5, 10), (8, 9)]\n"
     ]
    }
   ],
   "source": [
    "print(\"###### Add edges to graph ######\")\n",
    "for i, j in [(1,2), (1,3), (1,4), (2,7), (2,5), (5,8), (5,10), (8,9)]:\n",
    "    G.add_edge(i, j)\n",
    "print(\"Graph: \", G.edges)"
   ]
  },
  {
   "source": [
    "### Visualization"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# 정점의 위치 결정\n",
    "pos = nx.spring_layout(G)\n",
    "\n",
    "# 정점의 색과 크기 지정\n",
    "im = nx.draw_networkx_nodes(G, pos, node_color=\"red\", node_size=300)\n",
    "\n",
    "# 간선 출력\n",
    "nx.draw_networkx_edges(G, pos)\n",
    "\n",
    "# 각 정점의 라벨 출력\n",
    "nx.draw_networkx_labels(G, pos, font_size=15, font_color=\"black\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}