CNA_casestudy_AA_SC_P2MCA21707470.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNDCwSKz34wxpyjzCl/2NH3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/krish-Algometrix/Machine-Learning-Projects/blob/main/CNA_casestudy_AA_SC_P2MCA21707470.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Case Study :  Analysis of the karate club membership data that shows connections between members and probable groupings due to their connections.\n",
        "\n",
        "Citation : This data set is part of the accompanying material of the book\n",
        "                 \"Complex Networks: Principles, Methods and Applications\", \n",
        "                 V. Latora, V. Nicosia, G. Russo, Cambridge University Press (2017)ISBN: 9781107103184"
      ],
      "metadata": {
        "id": "it10YOlsG27M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing required libraries for perfroming the tasks\n",
        "\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import csv\n",
        "import urllib\n",
        "import csv\n",
        "from operator import itemgetter\n",
        "\n"
      ],
      "metadata": {
        "id": "KihA3RQdGsZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwJ_PGjQUGHi",
        "outputId": "2a7efd42-7779-42c3-aff0-014abb64d4d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = ('/content/drive/MyDrive/kac_data.txt')\n"
      ],
      "metadata": {
        "id": "dFqzrkfmu1VI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "karate_dataset = nx.read_edgelist('kac_data.txt', nodetype=str)"
      ],
      "metadata": {
        "id": "tubSCi7qc3NJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(karate_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EmSIkg_2TRx",
        "outputId": "5c027c5f-c113-4dd9-9c6b-5771da02bd94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "networkx.classes.graph.Graph"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nx.info(karate_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "3h_tvrZT2TJs",
        "outputId": "ffcea4aa-e902-44a5-c500-e125354ec6ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Graph with 34 nodes and 78 edges'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing the networtk of karate club members "
      ],
      "metadata": {
        "id": "crMIdv9q32ao"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nx.draw_networkx(karate_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "1M3hhHxH28yN",
        "outputId": "24942733-a4bc-4cca-8183-e47395850d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# measuring the Density of the Karate Club network which is a undirected graph\n",
        "density_karate_club = nx.density(karate_dataset)\n",
        "print(density_karate_club)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BL-_Gr8l5CXT",
        "outputId": "a728302d-427a-4b44-d050-a7df9021e2b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.13903743315508021\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# degree centrality of the network\n",
        "kcentrality= nx.degree_centrality(karate_dataset)\n",
        "kcentrality\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySo8gopQ5b7j",
        "outputId": "c34006c0-fbde-49c9-c954-a056a405f781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 0.48484848484848486,\n",
              " '1': 0.2727272727272727,\n",
              " '2': 0.30303030303030304,\n",
              " '3': 0.18181818181818182,\n",
              " '4': 0.09090909090909091,\n",
              " '5': 0.12121212121212122,\n",
              " '6': 0.12121212121212122,\n",
              " '7': 0.12121212121212122,\n",
              " '8': 0.15151515151515152,\n",
              " '9': 0.06060606060606061,\n",
              " '10': 0.09090909090909091,\n",
              " '11': 0.030303030303030304,\n",
              " '12': 0.06060606060606061,\n",
              " '13': 0.15151515151515152,\n",
              " '33': 0.5151515151515151,\n",
              " '14': 0.06060606060606061,\n",
              " '32': 0.36363636363636365,\n",
              " '15': 0.06060606060606061,\n",
              " '16': 0.06060606060606061,\n",
              " '17': 0.06060606060606061,\n",
              " '18': 0.06060606060606061,\n",
              " '19': 0.09090909090909091,\n",
              " '21': 0.06060606060606061,\n",
              " '23': 0.15151515151515152,\n",
              " '25': 0.09090909090909091,\n",
              " '27': 0.12121212121212122,\n",
              " '24': 0.09090909090909091,\n",
              " '28': 0.09090909090909091,\n",
              " '29': 0.12121212121212122,\n",
              " '26': 0.06060606060606061,\n",
              " '30': 0.12121212121212122,\n",
              " '31': 0.18181818181818182,\n",
              " '20': 0.06060606060606061,\n",
              " '22': 0.06060606060606061}"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_key = max(kcentrality, key = lambda x: kcentrality[x])\n",
        "max_key\n",
        "# The member 33 is the member to whom all other karate club members are connected."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "2haRui-1-c9N",
        "outputId": "b4f19ceb-9041-439a-fd02-2d3a07659280"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'33'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nx.clustering(karate_dataset)\n",
        "# we clearly find three clusters of karate_member groups ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oofxz_KUBbzk",
        "outputId": "99f84e78-0d23-4bbf-a542-8890704e86da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 0.15,\n",
              " '1': 0.3333333333333333,\n",
              " '2': 0.24444444444444444,\n",
              " '3': 0.6666666666666666,\n",
              " '4': 0.6666666666666666,\n",
              " '5': 0.5,\n",
              " '6': 0.5,\n",
              " '7': 1.0,\n",
              " '8': 0.5,\n",
              " '9': 0,\n",
              " '10': 0.6666666666666666,\n",
              " '11': 0,\n",
              " '12': 1.0,\n",
              " '13': 0.6,\n",
              " '33': 0.11029411764705882,\n",
              " '14': 1.0,\n",
              " '32': 0.19696969696969696,\n",
              " '15': 1.0,\n",
              " '16': 1.0,\n",
              " '17': 1.0,\n",
              " '18': 1.0,\n",
              " '19': 0.3333333333333333,\n",
              " '21': 1.0,\n",
              " '23': 0.4,\n",
              " '25': 0.3333333333333333,\n",
              " '27': 0.16666666666666666,\n",
              " '24': 0.3333333333333333,\n",
              " '28': 0.3333333333333333,\n",
              " '29': 0.6666666666666666,\n",
              " '26': 1.0,\n",
              " '30': 0.5,\n",
              " '31': 0.2,\n",
              " '20': 1.0,\n",
              " '22': 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cliques \n",
        "list(nx.find_cliques(karate_dataset))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48kwaDZKF-Z5",
        "outputId": "314abdc1-33c9-4f36-fb83-e5ea994fcdd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['21', '1', '0'],\n",
              " ['25', '31', '24'],\n",
              " ['25', '23'],\n",
              " ['3', '0', '2', '1', '13'],\n",
              " ['3', '0', '2', '1', '7'],\n",
              " ['3', '0', '12'],\n",
              " ['11', '0'],\n",
              " ['33', '32', '30', '8'],\n",
              " ['33', '32', '14'],\n",
              " ['33', '32', '18'],\n",
              " ['33', '32', '20'],\n",
              " ['33', '32', '31'],\n",
              " ['33', '32', '23', '29'],\n",
              " ['33', '32', '15'],\n",
              " ['33', '32', '22'],\n",
              " ['33', '27', '23'],\n",
              " ['33', '19'],\n",
              " ['33', '28', '31'],\n",
              " ['33', '26', '29'],\n",
              " ['33', '13'],\n",
              " ['33', '9'],\n",
              " ['5', '6', '16'],\n",
              " ['5', '6', '0'],\n",
              " ['5', '10', '0'],\n",
              " ['6', '4', '0'],\n",
              " ['4', '10', '0'],\n",
              " ['1', '30'],\n",
              " ['1', '0', '19'],\n",
              " ['1', '0', '17'],\n",
              " ['2', '32', '8'],\n",
              " ['2', '8', '0'],\n",
              " ['2', '27'],\n",
              " ['2', '28'],\n",
              " ['2', '9'],\n",
              " ['24', '27'],\n",
              " ['0', '31']]"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nx.eccentricity(karate_dataset) # the distance of each node from the zero node."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDVeEz91LW29",
        "outputId": "301bd5b2-b01c-419e-a51e-22d98d481ccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 3,\n",
              " '1': 3,\n",
              " '2': 3,\n",
              " '3': 3,\n",
              " '4': 4,\n",
              " '5': 4,\n",
              " '6': 4,\n",
              " '7': 4,\n",
              " '8': 3,\n",
              " '9': 4,\n",
              " '10': 4,\n",
              " '11': 4,\n",
              " '12': 4,\n",
              " '13': 3,\n",
              " '33': 4,\n",
              " '14': 5,\n",
              " '32': 4,\n",
              " '15': 5,\n",
              " '16': 5,\n",
              " '17': 4,\n",
              " '18': 5,\n",
              " '19': 3,\n",
              " '21': 4,\n",
              " '23': 5,\n",
              " '25': 4,\n",
              " '27': 4,\n",
              " '24': 4,\n",
              " '28': 4,\n",
              " '29': 5,\n",
              " '26': 5,\n",
              " '30': 4,\n",
              " '31': 3,\n",
              " '20': 5,\n",
              " '22': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sim = nx.simrank_similarity(karate_dataset, source=None, target=None, importance_factor=0.9, max_iterations=1000, tolerance=0.0001)"
      ],
      "metadata": {
        "id": "XcYsWMxZhnvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "sim_array = np.array([[sim[u][v] for v in karate_dataset ]for u in karate_dataset])\n",
        "sim_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqWTnNphkZsM",
        "outputId": "5085f379-b8ff-4c18-ff24-48aab5b8ec24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.32598544, 0.27460966, ..., 0.20810156, 0.19815335,\n",
              "        0.19815335],\n",
              "       [0.32598544, 1.        , 0.29358947, ..., 0.23055129, 0.21214484,\n",
              "        0.21214484],\n",
              "       [0.27460966, 0.29358947, 1.        , ..., 0.27463973, 0.26986475,\n",
              "        0.26986475],\n",
              "       ...,\n",
              "       [0.20810156, 0.23055129, 0.27463973, ..., 1.        , 0.37531061,\n",
              "        0.37531061],\n",
              "       [0.19815335, 0.21214484, 0.26986475, ..., 0.37531061, 1.        ,\n",
              "        0.6125208 ],\n",
              "       [0.19815335, 0.21214484, 0.26986475, ..., 0.37531061, 0.6125208 ,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis of karate club network relationships reveals that primarily there are clear groupings among the karate club members which may be due to experience/earlier batches and also specific skills and seniority.\n",
        "\n",
        "The clustering of the network results in clearly at least three major groups.\n"
      ],
      "metadata": {
        "id": "cs7Q-HHLoipQ"
      }
    }
  ]
}