hw1/src/q1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "sns.reset_defaults()\n",
    "sns.set_theme(rc={'figure.dpi': 72, 'savefig.dpi': 300,\n",
    "              'figure.autolayout': True})\n",
    "sns.set_style('ticks')\n",
    "sns.set_context('paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ErdosRenyi(n: int, m: int, seed: int = 2) -> dict:\n",
    "    \"\"\"Erdos-Renyi Random Graph\n",
    "\n",
    "    Args:\n",
    "        n (int): Number of vertices.\n",
    "        m (int): Number of edges.\n",
    "        seed (int, optional): Defaults to 2.\n",
    "\n",
    "    Returns:\n",
    "        dict: Adjacency list.\n",
    "    \"\"\"\n",
    "    random.seed(seed)\n",
    "    V = {i: set() for i in range(n)}\n",
    "    for i in range(m):\n",
    "        u, v = random.sample(list(V.keys()), k=2)\n",
    "        V[u].add(v)\n",
    "        V[v].add(u)\n",
    "    return V\n",
    "\n",
    "\n",
    "def SmallWorld(n: int, m: int, p: float, seed: int = 2) -> dict:\n",
    "    \"\"\"Small-World Model (Watts-Strogatz)\n",
    "\n",
    "    Args:\n",
    "        n (int): Number of vertices.\n",
    "        m (int): Number of edges.\n",
    "        p (float): Randomness probability.\n",
    "        seed (int, optional): Defaults to 2.\n",
    "\n",
    "    Returns:\n",
    "        dict: Adjacency list.\n",
    "    \"\"\"\n",
    "    random.seed(seed)\n",
    "    V = {i: set() for i in range(n)}\n",
    "    E = set()\n",
    "\n",
    "    # Create a ring as the lattice.\n",
    "    mcnt = 0\n",
    "    i = 0\n",
    "    k = 0\n",
    "    while True:\n",
    "        if mcnt >= m:\n",
    "            break\n",
    "        if i == 0:\n",
    "            k += 1\n",
    "        j = (i + k) % n\n",
    "        V[i].add(j)\n",
    "        V[j].add(i)\n",
    "        E.add(frozenset((i, j)))\n",
    "        mcnt += 1\n",
    "        i = (i + 1) % n\n",
    "\n",
    "    # Rewire\n",
    "    for i, j in E:\n",
    "        if random.random() > p:\n",
    "            continue\n",
    "        k = random.sample(list(V.keys()), k=1)[0]\n",
    "        if frozenset((i, k)) in E:\n",
    "            continue\n",
    "        V[i].remove(j)\n",
    "        V[j].remove(i)\n",
    "        V[i].add(k)\n",
    "        V[k].add(i)\n",
    "    return V\n",
    "\n",
    "\n",
    "def ReadUG(file_path: str, sep: str = None) -> dict:\n",
    "    \"\"\"Read Undirected Graph\n",
    "\n",
    "    Returns:\n",
    "        dict: Adjacency list.\n",
    "    \"\"\"\n",
    "    V = {}\n",
    "    with open(file_path) as f:\n",
    "        for l in f:\n",
    "            try:\n",
    "                u, v = map(int, l.split(sep))\n",
    "            except:\n",
    "                continue\n",
    "            V.setdefault(u, set()).add(v)\n",
    "            V.setdefault(v, set()).add(u)\n",
    "    return V\n",
    "\n",
    "\n",
    "def deg_dist(V: dict) -> dict:\n",
    "    \"\"\"Degree Distribution\n",
    "\n",
    "    Args:\n",
    "        V (dict): Adjacency list.\n",
    "\n",
    "    Returns:\n",
    "        dict: <k: N_k/N>\n",
    "    \"\"\"\n",
    "    degs = list(map(lambda k: len(V[k]), V))\n",
    "    return {d: degs.count(d)/len(V) for d in degs}\n",
    "\n",
    "\n",
    "def ccm(V: dict) -> float:\n",
    "    \"\"\"Clustering Coefficient Mean\n",
    "\n",
    "    Args:\n",
    "        V (dict): Adjacency list.\n",
    "\n",
    "    Returns:\n",
    "        float: Mean of Clustering Coefficient.\n",
    "    \"\"\"\n",
    "    C = []\n",
    "    for i in V:\n",
    "        ei = 0\n",
    "        ki = len(V[i])\n",
    "        if ki < 2:\n",
    "            continue\n",
    "        for j in V[i]:\n",
    "            for k in V[j]:\n",
    "                if k in V[i]:\n",
    "                    ei += 1\n",
    "        C.append(ei / (ki * (ki-1)))\n",
    "    return sum(C) / len(C)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erdos-Renyi's Clustering Coefficient: 0.0008\n",
      "Small-World's Clustering Coefficient: 0.2861\n",
      "LastFM Asia's Clustering Coefficient: 0.2850\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphs = (\n",
    "    ('Erdos-Renyi', ErdosRenyi(7624, 27806)),\n",
    "    ('Small-World', SmallWorld(7624, 27806, 0.24)),\n",
    "    ('LastFM Asia', ReadUG('datasets/lastfm_asia.csv', ',')),\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
    "for i, (l, V) in enumerate(graphs):\n",
    "    print(f\"{l}'s Clustering Coefficient: {ccm(V):.4f}\")\n",
    "    dd = deg_dist(V)\n",
    "    sns.scatterplot(x=dd.keys(), y=dd.values(), ax=ax[i])\n",
    "    ax[i].set(xscale=\"log\", yscale=\"log\", title=l, xlabel=\"k\", ylabel=\"p(k)\")\n",
    "fig.savefig('q1.png')\n",
    "fig\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.10.2 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2b324498af64d22b4773901be112d66dec816013b7f64fed368c8550f7daba2d"
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 },
 "nbformat": 4,
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}