machine learning/Igraph.ipynb

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      "name": "python3",
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      "cell_type": "markdown",
      "source": [
        "# Brief introduction to python iGraph library\n",
        "\n",
        "![igraph python](https://images.unsplash.com/photo-1566443280617-35db331c54fb?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=774&q=80)\n",
        "\n",
        "\n",
        "photo by [Omar Flores](https://unsplash.com/@__itsflores) at [Unsplash](https://unsplash.com/)\n",
        "\n",
        "\n",
        "## Introduction\n",
        "\n",
        "this is a very quick introduction about how to use python igraph library"
      ],
      "metadata": {
        "id": "CjUiSzEMgN13"
      }
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JHazlzT8mc1t"
      },
      "outputs": [],
      "source": [
        "#import\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install igraph\n",
        "!apt install libcairo2-dev\n",
        "!pip install pycairo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sP-jPK48mtR1",
        "outputId": "3cd1931d-fed8-40ec-f99c-20a5bf37a4ee"
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting igraph\n",
            "  Downloading igraph-0.9.11-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
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            "\u001b[?25hCollecting texttable>=1.6.2\n",
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            "Installing collected packages: texttable, igraph\n",
            "Successfully installed igraph-0.9.11 texttable-1.6.4\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  libcairo-script-interpreter2 libpixman-1-dev libxcb-shm0-dev\n",
            "Suggested packages:\n",
            "  libcairo2-doc\n",
            "The following NEW packages will be installed:\n",
            "  libcairo-script-interpreter2 libcairo2-dev libpixman-1-dev libxcb-shm0-dev\n",
            "0 upgraded, 4 newly installed, 0 to remove and 20 not upgraded.\n",
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            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxcb-shm0-dev amd64 1.13-2~ubuntu18.04 [6,684 B]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libcairo2-dev amd64 1.15.10-2ubuntu0.1 [626 kB]\n",
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            "Setting up libcairo2-dev:amd64 (1.15.10-2ubuntu0.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.5) ...\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pycairo\n",
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            "Building wheels for collected packages: pycairo\n",
            "  Building wheel for pycairo (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycairo: filename=pycairo-1.21.0-cp37-cp37m-linux_x86_64.whl size=286820 sha256=7483cd9f18260e344b26d04ed9d75a9be61a1450d77dbe7b563061bd6aba565b\n",
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            "Successfully installed pycairo-1.21.0\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from igraph import *\n",
        "#print(igraph.__version__)"
      ],
      "metadata": {
        "id": "E0LwJFFLm9zv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a graph from scratch\n",
        "g = Graph()\n",
        "#add vertices\n",
        "g.add_vertices(3)\n",
        "#add egdges\n",
        "g.add_edges([(0,1), (1,2)])\n",
        "#check the graph\n",
        "print(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81kNE6uVnEmE",
        "outputId": "429fc051-0760-4f93-f911-99ebbde05622"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IGRAPH U--- 3 2 --\n",
            "+ edges:\n",
            "0--1 1--2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#generate a tree\n",
        "#Graph.Tree() generates a regular tree graph\n",
        "#each node has two childreen\n",
        "g = Graph.Tree(22, 2)\n",
        "summary(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k7Sth5GnuWk",
        "outputId": "cffb6e2b-5dd6-4609-87f5-1385fc962efb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IGRAPH U--- 22 21 -- \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#the list of edges\n",
        "g.get_edgelist()[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5b2KGVzoBWc",
        "outputId": "f5351626-f7a4-4e01-a6f2-28837437e07b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 1),\n",
              " (0, 2),\n",
              " (1, 3),\n",
              " (1, 4),\n",
              " (2, 5),\n",
              " (2, 6),\n",
              " (3, 7),\n",
              " (3, 8),\n",
              " (4, 9),\n",
              " (4, 10)]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#assign attribute to a graph\n",
        "g[\"name\"] = \"social network\"\n",
        "# node and edges attributes\n",
        "g = Graph([(0,1), (0,2), (2,3), (3,4), (4,2), (2,5), (5,0), (6,3), (5,6)])\n",
        "#node attributes\n",
        "g.vs\n",
        "g.vs[\"name\"] = [\"Alice\", \"Bob\", \"Claire\", \"Dennis\", \"Esther\", \"Frank\", \"George\"]\n",
        "g.vs[\"age\"] = [25, 31, 18, 47, 22, 23, 50]\n",
        "g.vs[\"gender\"] = [\"f\", \"m\", \"f\", \"m\", \"f\", \"m\", \"m\"]\n",
        "#edges attribute\n",
        "g.es[\"is_formal\"] = [False, False, True, True, True, False, True, False, False]\n",
        "#print attributes for a edge\n",
        "g.es[0].attributes()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHu6y3sToLR1",
        "outputId": "0768f67f-8abb-4919-f9a3-644c9ac6b49a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'is_formal': False}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#node and edge attributes are modifiable\n",
        "g.es[0][\"is_formal\"] = True\n",
        "g.es[0].attributes()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylw5qCdrpA7P",
        "outputId": "7bf02aa9-2abc-4891-f9cf-cb49ffd42fd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'is_formal': True}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#structural properties\n",
        "#node degrees\n",
        "print(g.degree())\n",
        "#for a node\n",
        "print(g.degree(6))\n",
        "#for a list of node\n",
        "g.degree([2,3,4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLf-dxrKqZ2Q",
        "outputId": "04a0b189-2249-4ae5-e62f-a534508743e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3, 1, 4, 3, 2, 3, 2]\n",
            "2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4, 3, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#betweness centrality\n",
        "g.edge_betweenness()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wUC2zuxqm9f",
        "outputId": "e608c750-f35e-40f5-eb5c-fc64c0704d19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6.0, 6.0, 4.0, 2.0, 4.0, 3.0, 4.0, 3.0, 4.0]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#node selection\n",
        "g.vs.select(_degree=g.maxdegree())[\"name\"]\n",
        "claire = g.vs.find(name=\"Claire\")\n",
        "claire.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hoV7gQc7rCqv",
        "outputId": "1d49ca8f-a6d2-455d-800b-75ea42edf2f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get adjacency matrix\n",
        "A = g.get_adjacency()\n",
        "print(A)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3riFlz2KrxRi",
        "outputId": "0d6ead64-304e-488b-a71d-677fd27071b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0, 1, 1, 0, 0, 1, 0]\n",
            " [1, 0, 0, 0, 0, 0, 0]\n",
            " [1, 0, 0, 1, 1, 1, 0]\n",
            " [0, 0, 1, 0, 1, 0, 1]\n",
            " [0, 0, 1, 1, 0, 0, 0]\n",
            " [1, 0, 1, 0, 0, 0, 1]\n",
            " [0, 0, 0, 1, 0, 1, 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot with different layaout\n",
        "layout = g.layout_kamada_kawai()\n",
        "layout = g.layout(\"kamada_kawai\")\n",
        "plot(g, layout=layout, bbox=(300, 300))"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "EjEeRm-6r4uW",
        "outputId": "146e3d1c-7a45-42b5-f448-20b7b1aea195"
      },
      "execution_count": null,
      "outputs": [
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              "<igraph.drawing.Plot at 0x7f883bebe690>"
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      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots()\n",
        "plot(g, layout=layout, target=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "nGHJnZwFsPv-",
        "outputId": "99b4618c-7fbf-41df-e082-89b69f060957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g.vs[\"label\"] = g.vs[\"name\"]\n",
        "color_dict = {\"m\": \"blue\", \"f\": \"pink\"}\n",
        "g.vs[\"color\"] = [color_dict[gender] for gender in g.vs[\"gender\"]]\n",
        "plot(g, layout=layout, bbox=(300, 300), margin=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "hlWb23Y8vNBl",
        "outputId": "232575c5-06ef-4abe-bc14-3ec2ccebb498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<igraph.drawing.Plot at 0x7f883c3b79d0>"
            ],
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          "metadata": {
            "image/svg+xml": {
              "isolated": true
            }
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visual_style = {}\n",
        "visual_style[\"vertex_size\"] = 20\n",
        "visual_style[\"vertex_color\"] = [color_dict[gender] for gender in g.vs[\"gender\"]]\n",
        "visual_style[\"vertex_label\"] = g.vs[\"name\"]\n",
        "visual_style[\"edge_width\"] = [1 + 2 * int(is_formal) for is_formal in g.es[\"is_formal\"]]\n",
        "visual_style[\"layout\"] = layout\n",
        "visual_style[\"bbox\"] = (300, 300)\n",
        "visual_style[\"margin\"] = 20\n",
        "plot(g, **visual_style)"
      ],
      "metadata": {
        "id": "_UIQhGT0wH0l",
        "outputId": "8c066866-e810-457c-d5b0-4acc4eb19454",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<igraph.drawing.Plot at 0x7f883bf20450>"
            ],
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