SNA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "g = nx.Graph()\n",
        "g.add_node(\"A\")\n",
        "g.add_nodes_from([\"B\", \"C\", \"D\", \"E\"])\n",
        "g.add_edge(\"A\", \"B\")\n",
        "g.add_edges_from([(\"A\", \"C\"), (\"E\", \"C\"), (\"E\", \"D\"), (\"A\", \"D\")])\n",
        "print(g.nodes)\n",
        "print(g.edges)\n",
        "\n",
        "nx.draw(g, with_labels=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "meGft0-aZuIk",
        "outputId": "c1c158d4-9de7-4180-9282-23b7030d0244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A', 'B', 'C', 'D', 'E']\n",
            "[('A', 'B'), ('A', 'C'), ('A', 'D'), ('C', 'E'), ('D', 'E')]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Degree Centrality\n",
        "for node in g.nodes:\n",
        "    print(node, g.degree(node)/(len(g.nodes)-1))\n",
        "\n",
        "# Closeness Centrality\n",
        "adj = [[0, 1, 1, 1, 2], [1, 0, 2, 2, 3], [1, 2, 0, 2, 1], [1, 2, 2, 0, 1], [2, 3, 1, 1, 0]]\n",
        "for node in range(len(g.nodes)):\n",
        "    print(1/sum(adj[node]))\n",
        "\n",
        "# Betweenness Centrality\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIZ5GHAGYNYj",
        "outputId": "47c7f252-d9ff-4112-c6f5-58a89328fbfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A 0.75\n",
            "B 0.25\n",
            "C 0.5\n",
            "D 0.5\n",
            "E 0.5\n",
            "0.2\n",
            "0.125\n",
            "0.16666666666666666\n",
            "0.16666666666666666\n",
            "0.14285714285714285\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# BFS\n",
        "def bfs(graph, start):\n",
        "    node = start\n",
        "    queue = []\n",
        "    visited = []\n",
        "    while len(visited) < len(graph.nodes):\n",
        "        print(node)\n",
        "        visited.append(node)\n",
        "        for nod in graph.adj[node]:\n",
        "            if nod not in visited:\n",
        "                queue.append(nod)\n",
        "        node = queue.pop(0)\n",
        "\n",
        "# DFS\n",
        "def dfs(graph, node, visited=[]):\n",
        "    print(node)\n",
        "    visited.append(node)\n",
        "    for nod in graph.adj[node]:\n",
        "        if nod not in visited:\n",
        "            dfs(graph, nod, visited)\n",
        "\n",
        "bfs(g, \"A\")\n",
        "print()\n",
        "dfs(g, \"A\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yy9SEougZt0H",
        "outputId": "5381cf05-1ea0-4b8c-a40c-fa75c40ffee6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A\n",
            "B\n",
            "C\n",
            "D\n",
            "E\n",
            "\n",
            "A\n",
            "B\n",
            "C\n",
            "E\n",
            "D\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "upeslLd_EBV5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Trim edges for Island Method\n",
        "def trim(g, weight=1):\n",
        "    g2 = nx.Graph()\n",
        "    for i, j, edge in g.edges(data=True):\n",
        "        if edge[\"weight\"] > weight:\n",
        "            g2.add_edge(i, j, edge)\n",
        "    return g2\n",
        "\n",
        "def island(g, iter=5):\n",
        "    weights = [edge[\"weight\"] for i, j, edge in g.edges(data=True)]\n",
        "    min_wt = min(weights)\n",
        "    max_wt = max(weights)\n",
        "    step = int((max_wt - min_wt)/iter)\n",
        "    return [[lvl, trim(g, lvl)] for lvl in range(min_wt, max_wt, step)]\n",
        "\n",
        "islands = island(g, 5)\n",
        "for i in islands:\n",
        "    print(i[0], len(i[1]), len(nx.connected_component_subgraphs(i[0])))\n"
      ],
      "metadata": {
        "id": "Yc8OGcDTjsnP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NGpOaiwQDWch"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}