{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare interactions graphs\n",
    "\n",
    "Inspecting interaction and comparing graphs by counting the components, determining the connectivity, clustering and various other metrics. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "casename1 = '...'\n",
    "case1 = '...'\n",
    "\n",
    "casename2 = '...'\n",
    "case2 = '...'\n",
    "\n",
    "# Import the graph\n",
    "G1 = nx.read_graphml(f'../datasets/{case1}-dtc-graph.graphml')\n",
    "G2 = nx.read_graphml(f'../datasets/{case2}-dtc-graph.graphml')\n",
    "\n",
    "# Print nodes, edges and density of the graphs\n",
    "print(f'G1:\\nNodes: {G1.number_of_nodes()}\\nEdges: {G1.number_of_edges()}\\nDensity: {nx.density(G1):.5f}')\n",
    "print(f'G2:\\nNodes: {G2.number_of_nodes()}\\nEdges: {G2.number_of_edges()}\\nDensity: {nx.density(G2):.5f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove isolated nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G1.remove_nodes_from(list(nx.isolates(G1)))\n",
    "G2.remove_nodes_from(list(nx.isolates(G2)))\n",
    "\n",
    "# Print nodes, edges and density of the graphs\n",
    "print(f'G1:\\nNodes: {G1.number_of_nodes()}\\nEdges: {G1.number_of_edges()}\\nDensity: {nx.density(G1):.5f}')\n",
    "print(f'G2:\\nNodes: {G2.number_of_nodes()}\\nEdges: {G2.number_of_edges()}\\nDensity: {nx.density(G2):.5f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute average degree, connectivity, clustering and degree_assortivity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "components1 = list(nx.connected_components(G1))\n",
    "\n",
    "component_metrics1 = []\n",
    "\n",
    "for component in components1:\n",
    "\n",
    "    subG = G1.subgraph(component)\n",
    "\n",
    "    num_nodes = subG.number_of_nodes()\n",
    "    num_edges = subG.number_of_edges()\n",
    "    average_degree = (2 * num_edges) / num_nodes\n",
    "    connectivity = nx.node_connectivity(subG)\n",
    "    clustering = nx.average_clustering(subG)\n",
    "    degree_assortivity = nx.degree_assortativity_coefficient(subG)\n",
    "\n",
    "    metrics = {\n",
    "        'num_nodes': num_nodes,\n",
    "        'num_edges': num_edges,\n",
    "        'average_degree': average_degree,\n",
    "        'connectivity': connectivity,\n",
    "        'clustering': clustering,\n",
    "        'degree_assortivity': degree_assortivity,\n",
    "    }\n",
    "\n",
    "    component_metrics1.append(metrics)\n",
    "\n",
    "df_metric1 = pd.DataFrame(component_metrics1)\n",
    "df_metric1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "components2 = list(nx.connected_components(G2))\n",
    "\n",
    "component_metrics2 = []\n",
    "\n",
    "for component in components2:\n",
    "\n",
    "    subG = G2.subgraph(component)\n",
    "\n",
    "    num_nodes = subG.number_of_nodes()\n",
    "    num_edges = subG.number_of_edges()\n",
    "    average_degree = (2 * num_edges) / num_nodes\n",
    "    connectivity = nx.node_connectivity(subG)\n",
    "    clustering = nx.average_clustering(subG)\n",
    "    degree_assortivity = nx.degree_assortativity_coefficient(subG)\n",
    "\n",
    "    metrics = {\n",
    "        'num_nodes': num_nodes,\n",
    "        'num_edges': num_edges,\n",
    "        'average_degree': average_degree,\n",
    "        'connectivity': connectivity,\n",
    "        'clustering': clustering,\n",
    "        'degree_assortivity': degree_assortivity,\n",
    "    }\n",
    "\n",
    "    component_metrics2.append(metrics)\n",
    "\n",
    "df_metric2 = pd.DataFrame(component_metrics2)\n",
    "df_metric2.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out the outliers in the dataframe, based on number of edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metric1_ro = df_metric1[(df_metric1['num_edges'] < 400) & (df_metric1['num_nodes'] > 10)]\n",
    "\n",
    "df_metric1_ro.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metric2_ro = df_metric2[(df_metric2['num_edges'] < 400) & (df_metric2['num_nodes'] > 10)]\n",
    "\n",
    "df_metric2_ro.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distributions of average degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "upper_limit = 1e3\n",
    "\n",
    "# Define the number of bins\n",
    "num_bins = 20\n",
    "\n",
    "# Calculate the combined range for both datasets\n",
    "min_value = min(df_metric1_ro['average_degree'].min(), df_metric2_ro['average_degree'].min())\n",
    "max_value = max(df_metric1_ro['average_degree'].max(), df_metric2_ro['average_degree'].max())\n",
    "\n",
    "# Create bin edges\n",
    "bins = np.linspace(min_value, max_value, num_bins + 1)\n",
    "\n",
    "# Create a single plot\n",
    "sns.histplot(df_metric1_ro['average_degree'], bins=bins, color='blue', alpha=0.5, label=f'{casename1.title()} case')\n",
    "sns.histplot(df_metric2_ro['average_degree'], bins=bins, color='orange', alpha=0.5, label=f'{casename2.title()} case')\n",
    "\n",
    "# Set y-axis to logarithmic scale\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "\n",
    "# Set titles and labels\n",
    "plt.title('Distribution of average degree (nodes > 10, edges < 400)', fontsize=16)\n",
    "plt.xlabel('Average degree')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in', top=True, right=True)\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distributions of connectivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "upper_limit = 1e3\n",
    "\n",
    "# Define the number of bins\n",
    "num_bins = 20\n",
    "\n",
    "# Calculate the combined range for both datasets\n",
    "min_value = min(df_metric1_ro['connectivity'].min(), df_metric2_ro['connectivity'].min())\n",
    "max_value = max(df_metric1_ro['connectivity'].max(), df_metric2_ro['connectivity'].max())\n",
    "\n",
    "# Create bin edges\n",
    "bins = np.linspace(min_value, max_value, num_bins + 1)\n",
    "\n",
    "# Create a single plot\n",
    "sns.histplot(df_metric1_ro['connectivity'], bins=bins, color='blue', alpha=0.5, label=f'{casename1.title()} case')\n",
    "sns.histplot(df_metric2_ro['connectivity'], bins=bins, color='orange', alpha=0.5, label=f'{casename2.title()} case')\n",
    "\n",
    "# Set y-axis to logarithmic scale\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "\n",
    "# Set titles and labels\n",
    "plt.title('Distribution of connectivity (nodes > 10, edges < 400)', fontsize=16)\n",
    "plt.xlabel('Connectivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in', top=True, right=True)\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distributions of clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "upper_limit = 1e3\n",
    "\n",
    "# Define the number of bins\n",
    "num_bins = 20\n",
    "\n",
    "# Calculate the combined range for both datasets\n",
    "min_value = min(df_metric1_ro['clustering'].min(), df_metric2_ro['clustering'].min())\n",
    "max_value = max(df_metric1_ro['clustering'].max(), df_metric2_ro['clustering'].max())\n",
    "\n",
    "# Create bin edges\n",
    "bins = np.linspace(min_value, max_value, num_bins + 1)\n",
    "\n",
    "# Create a single plot\n",
    "sns.histplot(df_metric1_ro['clustering'], bins=bins, color='blue', alpha=0.5, label=f'{casename1.title()} case')\n",
    "sns.histplot(df_metric2_ro['clustering'], bins=bins, color='orange', alpha=0.5, label=f'{casename2.title()} case')\n",
    "\n",
    "# Set y-axis to logarithmic scale\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "\n",
    "# Set titles and labels\n",
    "plt.title('Distribution of clustering (nodes > 10, edges < 400)', fontsize=16)\n",
    "plt.xlabel('Clustering')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in', top=True, right=True)\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distributions of degree assortivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "upper_limit = 1e3\n",
    "\n",
    "# Define the number of bins\n",
    "num_bins = 20\n",
    "\n",
    "# Calculate the combined range for both datasets\n",
    "min_value = min(df_metric1_ro['degree_assortivity'].min(), df_metric2_ro['degree_assortivity'].min())\n",
    "max_value = max(df_metric1_ro['degree_assortivity'].max(), df_metric2_ro['degree_assortivity'].max())\n",
    "\n",
    "# Create bin edges\n",
    "bins = np.linspace(min_value, max_value, num_bins + 1)\n",
    "\n",
    "# Create a single plot\n",
    "sns.histplot(df_metric1_ro['degree_assortivity'], bins=bins, color='blue', alpha=0.5, label=f'{casename1.title()} case')\n",
    "sns.histplot(df_metric2_ro['degree_assortivity'], bins=bins, color='orange', alpha=0.5, label=f'{casename2.title()} case')\n",
    "\n",
    "# Set y-axis to logarithmic scale\n",
    "plt.yscale('log')\n",
    "plt.ylim(0.9, upper_limit)\n",
    "\n",
    "# Set titles and labels\n",
    "plt.title('Distribution of degree assortivity (nodes > 10, edges < 400)', fontsize=16)\n",
    "plt.xlabel('Degree assortivity')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', direction='in', top=True, right=True)\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.99])\n",
    "plt.show()"
   ]
  }
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