igraph_tutorial_09.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56420de0",
   "metadata": {},
   "source": [
    "# Python-igraph编程实践基础教程：igraph官方手册使用解读"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d7192ca",
   "metadata": {},
   "source": [
    "## igraph编程实践基础教程第09课：官方示例讲解-Quick Start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9690417e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !/usr/bin/python3\n",
    "# -*- coding: utf-8 -*-\n",
    "# Author ： 单哥的科研日常（公众号、B站）\n",
    "# 示例程序所用库的版本：igraph==0.10.4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a2ccc5",
   "metadata": {},
   "source": [
    "#### 对于着急入门igraph的初学者来说，这篇介绍将给你一个以下操作的快速概述：\n",
    "#### （1）建立一个图\n",
    "#### （2）设置节点和边的属性\n",
    "#### （3）使用matplotlib绘制图\n",
    "#### （4）将可视化的图保存下来\n",
    "#### （5）以.gml文件的形式导出和导入图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09b99de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import igraph as ig\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "507ad59b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造一个有5个顶点的图\n",
    "n_vertices = 5\n",
    "edges = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4), (3, 4)]\n",
    "g = ig.Graph(n_vertices, edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67314748",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置图、节点和边的属性\n",
    "g[\"title\"] = \"Small Social Network\"\n",
    "g.vs[\"name\"] = [\"Daniel Morillas\", \"Kathy Archer\", \"Kyle Ding\", \"Joshua Walton\", \"Jana Hoyer\"]\n",
    "g.vs[\"gender\"] = [\"M\", \"F\", \"F\", \"M\", \"F\"]\n",
    "g.es[\"married\"] = [False, False, False, False, False, False, False, True]\n",
    "\n",
    "# 更改已设置的节点和连边属性\n",
    "g.vs[1][\"name\"] = \"Kathy Morillas\"\n",
    "g.es[0][\"married\"] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f1ca83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 借助matplotlib绘图\n",
    "# 注意属性可以全局设置(例如vertex_size)，也可以使用数组单独设置(例如vertex_color)。\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "ig.plot(\n",
    "    g,\n",
    "    target=ax,\n",
    "    layout=\"circle\", # 设置布局为circular layout\n",
    "    vertex_size=0.5,\n",
    "    vertex_color=[\"blue\" if gender == \"M\" else \"orange\" for gender in g.vs[\"gender\"]],\n",
    "    vertex_frame_width=4.0,\n",
    "    vertex_frame_color=\"white\",\n",
    "    vertex_label=g.vs[\"name\"],\n",
    "    vertex_label_size=12.0,\n",
    "    edge_width=[2 if married else 1 for married in g.es[\"married\"]],\n",
    "    edge_color=[\"lime\" if married else \"gray\" for married in g.es[\"married\"]]\n",
    ")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# 将可视化的图保存为图文件\n",
    "fig.savefig('./attached files/social_network.png', dpi=300)\n",
    "fig.savefig('./attached files/social_network.jpg', dpi=300)\n",
    "fig.savefig('./attached files/social_network.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fcb8eca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python396\\lib\\site-packages\\igraph\\io\\files.py:494: RuntimeWarning: The boolean edge attribute 'married' was converted to numeric. at src/io/gml.c:1281\n",
      "  return writer(f, *args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "# 将图作为gml文件导出和导入\n",
    "g.save(\"./attached files/social_network.gml\")\n",
    "g = ig.load(\"./attached files/social_network.gml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43a572f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
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       "<g id=\"surface2\">\n",
       "<rect x=\"0\" y=\"0\" width=\"300\" height=\"300\" style=\"fill:rgb(100%,100%,100%);fill-opacity:1;stroke:none;\"/>\n",
       "<path style=\"fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(26.666667%,26.666667%,26.666667%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 280 143.863281 L 33.488281 148.347656 \"/>\n",
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       "<path style=\"fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(26.666667%,26.666667%,26.666667%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 33.488281 148.347656 L 273.804688 20 \"/>\n",
       "<path style=\"fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(26.666667%,26.666667%,26.666667%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 20 24.617188 L 273.804688 20 \"/>\n",
       "<path style=\"fill-rule:nonzero;fill:rgb(100%,0%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 290 143.863281 C 290 149.386719 285.523438 153.863281 280 153.863281 C 274.476563 153.863281 270 149.386719 270 143.863281 C 270 138.339844 274.476563 133.863281 280 133.863281 C 285.523438 133.863281 290 138.339844 290 143.863281 \"/>\n",
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      ],
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     },
     "execution_count": 7,
     "metadata": {
      "image/svg+xml": {
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   ],
   "source": [
    "ig.plot(g, bbox=(300, 300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a9c5316",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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