GIT_RandomNetworkProperties.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPGtM4I/9ejW3HEBjmBG0kU",
      "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/shima-aflatounian/K-core-Networks/blob/main/GIT_RandomNetworkProperties.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Take Erdos Reyni network as an example, given specific probability of edge creation, given its random DNA there will be semi-different generated networks of this family,so when we want to do network analysis on such networks, we need to do calculate average of network features among samples to have a statiscal valid saying.\n",
        "\n",
        "Description of this file\n",
        "1st we generated 20 samples of Erdos Renyi Network\n",
        "2nd we calcualte the network charactersitic of these graphs, as you see they are abit different in their features\n",
        "3rd we extract an average calculated fields for this graph, which further can be used for analysis as representative of this graph with specific size to compare with representative of other families."
      ],
      "metadata": {
        "id": "Y4fvRlLQtcFX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zpoYm6Wvr-qd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9781ff4f-cb32-4d63-9efe-7edadda15b9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "Average Results for Erdos-Renyi Graphs:\n",
            "Average Number of Nodes: 100.0\n",
            "Average Number of Edges: 986.1\n",
            "Average K-Core Range: 14.076500000000001\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# read the generated graphs\n",
        "\n",
        "import networkx as nx\n",
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "num_samples = 20\n",
        "save_directory = '/content/gdrive/My Drive/generated_networks/4'\n",
        "\n",
        "erdos_renyi_node_counts = []\n",
        "erdos_renyi_edge_counts = []\n",
        "erdos_renyi_kcore_ranges = []\n",
        "\n",
        "\n",
        "\n",
        "for i in range(num_samples):\n",
        "    # Read Erdos-Renyi graph\n",
        "    er_save_path = os.path.join(save_directory, f'er_sample_{i}.gml')\n",
        "    er_graph = nx.read_gml(er_save_path)\n",
        "    er_node_count = er_graph.number_of_nodes()\n",
        "    er_edge_count = er_graph.number_of_edges()\n",
        "    er_kcore_range = nx.core_number(er_graph)\n",
        "    er_kcore_values = list(er_kcore_range.values())\n",
        "\n",
        "    erdos_renyi_node_counts.append(er_node_count)\n",
        "    erdos_renyi_edge_counts.append(er_edge_count)\n",
        "    erdos_renyi_kcore_ranges.append(sum(er_kcore_values) / len(er_kcore_values))\n",
        "\n",
        "    # Read Watts-Strogatz graph\n",
        "\n",
        "# Calculate averages for Erdos-Renyi graphs\n",
        "avg_er_node_count = sum(erdos_renyi_node_counts) / len(erdos_renyi_node_counts)\n",
        "avg_er_edge_count = sum(erdos_renyi_edge_counts) / len(erdos_renyi_edge_counts)\n",
        "avg_er_kcore_range = sum(erdos_renyi_kcore_ranges) / len(erdos_renyi_kcore_ranges)\n",
        "\n",
        "# Calculate averages for Watts-Strogatz graphs\n",
        "\n",
        "# Print average results for Erdos-Renyi graphs\n",
        "print('Average Results for Erdos-Renyi Graphs:')\n",
        "print(f'Average Number of Nodes: {avg_er_node_count}')\n",
        "print(f'Average Number of Edges: {avg_er_edge_count}')\n",
        "print(f'Average K-Core Range: {avg_er_kcore_range}')\n",
        "print('')\n",
        "\n",
        "# Print average results for Watts-Strogatz graphs\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ": as you see they are abit different in their features 3rd we extract an average calculated fields for this graph, which further can be used for analysis as representative of this graph with specific size to compare with representative of other families."
      ],
      "metadata": {
        "id": "dwVzFc9Bu5q5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "num_samples = 20\n",
        "save_directory = '/content/gdrive/My Drive/generated_networks/4'\n",
        "\n",
        "erdos_renyi_node_counts = []\n",
        "erdos_renyi_edge_counts = []\n",
        "erdos_renyi_kcore = []\n",
        "results={}\n",
        "min_Lkcore=[]\n",
        "max_Lkcore=[]\n",
        "\n",
        "\n",
        "\n",
        "for i in range(num_samples):\n",
        "    # Read Erdos-Renyi graph\n",
        "    er_save_path = os.path.join(save_directory, f'er_sample_{i}.gml')\n",
        "    er_graph = nx.read_gml(er_save_path)\n",
        "    er_node_count = er_graph.number_of_nodes()\n",
        "    er_edge_count = er_graph.number_of_edges()\n",
        "    er_kcore = nx.core_number(er_graph)\n",
        "    er_kcore_values = list(er_kcore.values())\n",
        "\n",
        "    # Calculate k-core range\n",
        "#    kcore_range = nx.core_number(G)\n",
        "    kcore_values = er_kcore_values\n",
        "    min_kcore = min(kcore_values)\n",
        "    max_kcore = max(kcore_values)\n",
        "\n",
        "    min_Lkcore.append(min_kcore)\n",
        "    max_Lkcore.append(max_kcore)\n",
        "\n",
        "    avg_degree = sum(dict(er_graph.degree()).values()) / er_node_count\n",
        "    avg_distance = nx.average_shortest_path_length(er_graph)\n",
        "    correlation = nx.degree_pearson_correlation_coefficient(er_graph)\n",
        "    clustering_coefficient = nx.average_clustering(er_graph)\n",
        "\n",
        "    results[f'sample_{i}'] = {\n",
        "        'Nodes': er_node_count,\n",
        "        'Edges': er_edge_count,\n",
        "\n",
        "        'min_kcore': min_kcore,\n",
        "        'max_kcore': max_kcore,\n",
        "        'list_KcoreValues': kcore_values,\n",
        "        'avg_degree': avg_degree,\n",
        "        'avg_distance': avg_distance,\n",
        "        'correlation': correlation,\n",
        "        'clustering_coeff': clustering_coefficient\n",
        "\n",
        "\n",
        "    }\n",
        "\n",
        "    erdos_renyi_node_counts.append(er_node_count)\n",
        "    erdos_renyi_edge_counts.append(er_edge_count)\n",
        "    erdos_renyi_kcore.append((sum(er_kcore_values)) / (len(er_kcore_values)))\n",
        "\n",
        "    # Read Watts-Strogatz graph\n",
        "\n",
        "# Calculate averages for Erdos-Renyi graphs\n",
        "print(f'list min kcore: {min_Lkcore}')\n",
        "print(f'list max kcore: {max_Lkcore}')\n",
        "\n",
        "avg_Lkcore=[sum(min_Lkcore)/len(min_Lkcore),sum(max_Lkcore)/len(max_Lkcore)] #right approach to calc avg range among graph samples/version\n",
        "\n",
        "avg_er_node_count = sum(erdos_renyi_node_counts) / len(erdos_renyi_node_counts)\n",
        "avg_er_edge_count = sum(erdos_renyi_edge_counts) / len(erdos_renyi_edge_counts)\n",
        "avg_er_kcore = sum(erdos_renyi_kcore) / len(erdos_renyi_kcore)\n",
        "\n",
        "# Calculate averages for Watts-Strogatz graphs\n",
        "\n",
        "# Print average results for Erdos-Renyi graphs\n",
        "print('Average Results for Erdos-Renyi Graphs:')\n",
        "print(f'Average Number of Nodes: {avg_er_node_count}')\n",
        "print(f'Average Number of Edges: {avg_er_edge_count}')\n",
        "print(f'erdos_renyi_kcore : {erdos_renyi_kcore}')\n",
        "\n",
        "print(f'Average K-Core value : {avg_er_kcore}')\n",
        "print(f'Average K-Core Range : {avg_Lkcore}')\n",
        "print('')\n",
        "\n",
        "# Print results\n",
        "print('K-Core Range for Erdos-Renyi Graphs:')\n",
        "#for sample, kcore_range in results.items():\n",
        "#    print(f'{sample}:')\n",
        "#    print(f'  Min K-Core: {kcore_range[\"min_kcore\"]}')\n",
        "#    print(f'  Max K-Core: {kcore_range[\"max_kcore\"]}')\n",
        "##    print(f'  list K-Core: {kcore_range[\"list_KcoreValues\"]}')\n",
        "\n",
        "#    print('')\n",
        "\n",
        "print(f'{\"Sample Number\":<10} {\"Min K-Core\":<12} {\"Max K-Core\":<12}')\n",
        "\n",
        "#for sample, kcore_range in results.items():\n",
        "for sample, kcore_range in results.items():\n",
        "\n",
        "    Min_kCore = kcore_range[\"min_kcore\"]\n",
        "    Max_kCore = kcore_range[\"max_kcore\"]\n",
        "    Avg_Degree = kcore_range[\"avg_degree\"]\n",
        "    Avg_Distance = kcore_range[\"avg_degree\"]\n",
        "    Correlation = kcore_range[\"max_kcore\"]\n",
        "    Clustering_coeff = kcore_range[\"max_kcore\"]\n",
        "\n",
        "    print(f'{sample:<14} {Min_kCore:<12} {Max_kCore:<12} {Avg_Degree:<12} {Avg_Distance:<12} {Correlation:<12} {Clustering_coeff:<12}')\n"
      ],
      "metadata": {
        "id": "S37Femlanb0C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2d42e6e-1891-4f53-d7da-22e66f0cfc55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n",
            "list min kcore: [12, 11, 9, 12, 11, 9, 12, 12, 9, 8, 9, 12, 9, 12, 11, 9, 10, 11, 8, 10]\n",
            "list max kcore: [14, 14, 14, 14, 15, 15, 15, 14, 14, 14, 15, 14, 13, 15, 15, 14, 13, 15, 14, 14]\n",
            "Average Results for Erdos-Renyi Graphs:\n",
            "Average Number of Nodes: 100.0\n",
            "Average Number of Edges: 986.1\n",
            "erdos_renyi_kcore : [13.95, 13.87, 13.75, 13.92, 14.81, 14.83, 14.82, 13.91, 13.79, 13.76, 14.65, 13.9, 12.83, 14.88, 14.73, 13.71, 12.96, 14.91, 13.7, 13.85]\n",
            "Average K-Core value : 14.076500000000001\n",
            "Average K-Core Range : [10.3, 14.25]\n",
            "\n",
            "K-Core Range for Erdos-Renyi Graphs:\n",
            "Sample Number Min K-Core   Max K-Core  \n",
            "sample_0       12           14           19.56        19.56        14           14          \n",
            "sample_1       11           14           20.1         20.1         14           14          \n",
            "sample_2       9            14           19.14        19.14        14           14          \n",
            "sample_3       12           14           19.5         19.5         14           14          \n",
            "sample_4       11           15           19.8         19.8         15           15          \n",
            "sample_5       9            15           19.2         19.2         15           15          \n",
            "sample_6       12           15           20.48        20.48        15           15          \n",
            "sample_7       12           14           19.48        19.48        14           14          \n",
            "sample_8       9            14           19.04        19.04        14           14          \n",
            "sample_9       8            14           20.24        20.24        14           14          \n",
            "sample_10      9            15           20.2         20.2         15           15          \n",
            "sample_11      12           14           19.8         19.8         14           14          \n",
            "sample_12      9            13           18.66        18.66        13           13          \n",
            "sample_13      12           15           19.96        19.96        15           15          \n",
            "sample_14      11           15           20.38        20.38        15           15          \n",
            "sample_15      9            14           19.3         19.3         14           14          \n",
            "sample_16      10           13           19.18        19.18        13           13          \n",
            "sample_17      11           15           20.28        20.28        15           15          \n",
            "sample_18      8            14           19.6         19.6         14           14          \n",
            "sample_19      10           14           20.54        20.54        14           14          \n"
          ]
        }
      ]
    }
  ]
}