DSC291_NLA_PageRankvsMPR_Project_Main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "49739aa4",
   "metadata": {},
   "source": [
    "#### 0.1 Libraries for Linear Algebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98837c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "import scipy.sparse as sps\n",
    "from scipy.sparse import diags"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0803b5a",
   "metadata": {},
   "source": [
    "#### 0.2 Libraries for working with Graphs and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ea7e269",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import SVG\n",
    "import sknetwork as skn\n",
    "from sknetwork.visualization import svg_digraph\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "343ba01e",
   "metadata": {},
   "source": [
    "## 1. Load Input Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765146e2",
   "metadata": {},
   "source": [
    "### 1.1 Edge List --> Citation Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72f39b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def edge_list_from_network(network_file):\n",
    "    f = open(network_file, 'r')\n",
    "    content = map(lambda x: x[:-1].split(';'), f.readlines())\n",
    "    cite_net_edges = list(map(lambda x: (int(x[0]), int(x[0])), content))\n",
    "    f.close()\n",
    "    return cite_net_edges"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c264ef",
   "metadata": {},
   "source": [
    "## 2. Construst Adjacency Matrix \n",
    "<br>\n",
    "<span style=\"color:black;font-weight:400;font-size:15px;font-family:Arial\"> \n",
    "Type: CSR - Condensed Sparsed Rows | d-type: float64\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e411cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_adjacency_matrix(edgeList):\n",
    "    # edge_list will be a list of tuples of form (src id, dst id)\n",
    "    main_graph = skn.data.from_edge_list(edge_list=edgeList, directed=True) \n",
    "    # create the sparse adjacency matrix using the edge data\n",
    "    adjacency_matrix = main_graph.adjacency.astype(np.float64)\n",
    "    node_names = main_graph.names\n",
    "    return adjacency_matrix, node_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "845c4a43",
   "metadata": {},
   "source": [
    "## 3. Construst Random walk Transition Matrix \n",
    "<br>\n",
    "<span style=\"color:black;font-weight:400;font-size:15px;font-family:Arial\"> \n",
    "Type: CSR - Condensed Sparsed Rows | d-type: float64\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c39654c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_transition_matrix(adjMat):\n",
    "    for number in range(0, np.size(adjMat, 0)):\n",
    "        row_sum = adjMat.getrow(number).sum()\n",
    "        if row_sum == 0:\n",
    "            continue\n",
    "        else:\n",
    "            for number_col in adjMat.indices[adjMat.indptr[number]:adjMat.indptr[number+1]]:\n",
    "                adjMat[number, number_col] = adjMat[number, number_col] / row_sum\n",
    "    return adjMat.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f58c9b",
   "metadata": {},
   "source": [
    "## 4. PageRank Algorithm \n",
    "<br>\n",
    "<span style=\"color:black;font-weight:400;font-size:15px;font-family:Arial\"> \n",
    "    Vanilla PageRank Algorithm | Adapted from <i>pagerank_motif_direct.py</i> from <i>H.Zhao Et.Al</i>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b027c9c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_TIME = 30000\n",
    "# PageRank Algorithm\n",
    "def pageRank(p, m, v):\n",
    "    e = np.ones((m.shape[0], 1))\n",
    "    n = m.shape[0]\n",
    "    count = 0\n",
    "    while count <= MAX_TIME:\n",
    "        v = p * m.dot(v) + ((1 - p) / n) * e\n",
    "        count = count + 1\n",
    "    return v\n",
    "\n",
    "# Initializing PageRank Scores\n",
    "def firstPr(transitionMatrix):\n",
    "    pr = np.zeros((transitionMatrix.shape[0], 1), dtype=float)\n",
    "    for i in range(transitionMatrix.shape[0]):\n",
    "        pr[i] = float(1) / transitionMatrix.shape[0]\n",
    "    return pr\n",
    "\n",
    "#Computing PageRank Scores\n",
    "def compute_pagerank(adjacency_matrix, alpha):\n",
    "    T = get_transition_matrix(adjacency_matrix)\n",
    "    pr = firstPr(T)\n",
    "    p = 1 - alpha\n",
    "    return pageRank(p, T, pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14409c5a",
   "metadata": {},
   "source": [
    "## 5. Motif-Based PageRank Algorithm \n",
    "<br>\n",
    "<span style=\"color:black;font-weight:400;font-size:15px;font-family:Arial\"> \n",
    "    MPR Algorithm | Adapted from <i>pagerank_motif_direct.py</i> from <i>H.Zhao Et.Al</i>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1631f3a1",
   "metadata": {},
   "source": [
    "### 5.1 Motif Type\n",
    "<br>\n",
    "<span style=\"color:black;font-weight:400;font-size:15px;font-family:Arial\"> \n",
    "    <b>Motif Types Used:</b> <i>M1, M2, M3, M4, M5, M6, M7</i>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9902a5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def motif_types(adjacency_matrix, motif_order, motif_type):\n",
    "    if motif_type == 'M1':\n",
    "        for i in range(motif_order):\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            B_matrix_spare = adjacency_matrix.multiply(adjacency_tran)\n",
    "            U_matrix = adjacency_matrix - B_matrix_spare\n",
    "            U_matrix = np.abs(U_matrix)\n",
    "            U_tran = np.transpose(U_matrix)\n",
    "            result_1 = U_matrix.dot(U_matrix)\n",
    "            result_1 = result_1.multiply(U_tran)\n",
    "            adjacency_matrix = result_1\n",
    "            adjacency_matrix = normal_matrix_new(adjacency_matrix)\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            adjacency_matrix = adjacency_matrix + adjacency_tran\n",
    "    elif motif_type == 'M2':\n",
    "        for i in range(motif_order):\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            B_matrix_spare = adjacency_matrix.multiply(adjacency_tran)\n",
    "            U_matrix = adjacency_matrix - B_matrix_spare\n",
    "            U_matrix = np.abs(U_matrix)\n",
    "            U_tran = np.transpose(U_matrix)\n",
    "            result_1 = B_matrix_spare.dot(U_matrix)\n",
    "            result_1 = result_1.multiply(U_tran)\n",
    "            result_2 = U_matrix.dot(B_matrix_spare)\n",
    "            result_2 = result_2.multiply(U_tran)\n",
    "            result_3 = U_matrix.dot(U_matrix)\n",
    "            result_3 = result_3.multiply(B_matrix_spare)\n",
    "            adjacency_matrix = result_1 + result_2\n",
    "            adjacency_matrix = adjacency_matrix + result_3\n",
    "            adjacency_matrix = normal_matrix_new(adjacency_matrix)\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            adjacency_matrix = adjacency_matrix + adjacency_tran\n",
    "    elif motif_type == 'M3':\n",
    "        for i in range(motif_order):\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            B_matrix_spare = adjacency_matrix.multiply(adjacency_tran)\n",
    "            U_matrix = adjacency_matrix - B_matrix_spare\n",
    "            U_matrix = np.abs(U_matrix)\n",
    "            result_1 = B_matrix_spare.dot(B_matrix_spare)\n",
    "            result_1 = result_1.multiply(U_matrix)\n",
    "            result_2 = B_matrix_spare.dot(U_matrix)\n",
    "            result_2 = result_2.multiply(B_matrix_spare)\n",
    "            result_3 = U_matrix.dot(B_matrix_spare)\n",
    "            result_3 = result_3.multiply(B_matrix_spare)\n",
    "            adjacency_matrix = result_1 + result_2\n",
    "            adjacency_matrix = adjacency_matrix + result_3\n",
    "            adjacency_matrix = normal_matrix_new(adjacency_matrix)\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            adjacency_matrix = adjacency_matrix + adjacency_tran\n",
    "    elif motif_type == 'M4':\n",
    "        for i in range(motif_order):\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            B_matrix_spare = adjacency_matrix.multiply(adjacency_tran)\n",
    "            result_1 = B_matrix_spare.dot(B_matrix_spare)\n",
    "            result_1 = result_1.multiply(B_matrix_spare)\n",
    "            adjacency_matrix = result_1\n",
    "            adjacency_matrix = normal_matrix_new(adjacency_matrix)\n",
    "    elif motif_type == 'M5':\n",
    "        for i in range(motif_order):\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            B_matrix_spare = adjacency_matrix.multiply(adjacency_tran)\n",
    "            U_matrix = adjacency_matrix - B_matrix_spare\n",
    "            U_matrix = np.abs(U_matrix)\n",
    "            U_tran = np.transpose(U_matrix)\n",
    "            result_1 = U_matrix.dot(U_matrix)\n",
    "            result_1 = result_1.multiply(U_matrix)\n",
    "            result_2 = U_matrix.dot(U_tran)\n",
    "            result_2 = result_2.multiply(U_matrix)\n",
    "            result_3 = U_tran.dot(U_matrix)\n",
    "            result_3 = result_3.multiply(U_matrix)\n",
    "            adjacency_matrix = result_1 + result_2\n",
    "            adjacency_matrix = adjacency_matrix + result_3\n",
    "            adjacency_matrix = normal_matrix_new(adjacency_matrix)\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            adjacency_matrix = adjacency_matrix + adjacency_tran\n",
    "    elif motif_type == 'M6':\n",
    "        for i in range(motif_order):\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            B_matrix_spare = adjacency_matrix.multiply(adjacency_tran)\n",
    "            U_matrix = adjacency_matrix - B_matrix_spare\n",
    "            U_matrix = np.abs(U_matrix)\n",
    "            U_tran = np.transpose(U_matrix)\n",
    "            result_1 = U_matrix.dot(B_matrix_spare)\n",
    "            result_1 = result_1.multiply(U_matrix)\n",
    "            result_2 = B_matrix_spare.dot(U_tran)\n",
    "            result_2 = result_2.multiply(U_tran)\n",
    "            result_3 = U_tran.dot(U_matrix)\n",
    "            result_3 = result_3.multiply(B_matrix_spare)\n",
    "            adjacency_matrix = result_1 + result_2\n",
    "            adjacency_matrix = adjacency_matrix + result_3\n",
    "            adjacency_matrix = normal_matrix_new(adjacency_matrix)\n",
    "    elif motif_type == 'M7':\n",
    "        for i in range(motif_order):\n",
    "            adjacency_tran = np.transpose(adjacency_matrix)\n",
    "            B_matrix_spare = adjacency_matrix.multiply(adjacency_tran)\n",
    "            U_matrix = adjacency_matrix - B_matrix_spare\n",
    "            U_matrix = np.abs(U_matrix)\n",
    "            U_tran = np.transpose(U_matrix)\n",
    "            result_1 = U_tran.dot(B_matrix_spare)\n",
    "            result_1 = result_1.multiply(U_tran)\n",
    "            result_2 = B_matrix_spare.dot(U_matrix)\n",
    "            result_2 = result_2.multiply(U_matrix)\n",
    "            result_3 = U_matrix.dot(U_tran)\n",
    "            result_3 = result_3.multiply(B_matrix_spare)\n",
    "            adjacency_matrix = result_1 + result_2\n",
    "            adjacency_matrix = adjacency_matrix + result_3\n",
    "            adjacency_matrix = normal_matrix_new(adjacency_matrix)\n",
    "    return adjacency_matrix\n",
    "\n",
    "def normal_matrix_new(a):\n",
    "    # For symmetric matrices, we can use matrix multiplication to normalize, which is faster.\n",
    "    d = a.sum(0)\n",
    "    d = np.array(d)\n",
    "    d = d[0]\n",
    "    dd = list(map(lambda x: 0 if x==0 else np.power(x, -0.5), d))\n",
    "    D_matrix = diags(dd, 0)\n",
    "    C = D_matrix.dot(a)\n",
    "    C = C.dot(D_matrix)\n",
    "    a = C\n",
    "    return a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c95581",
   "metadata": {},
   "source": [
    "### 5.2 Construct Motifs\n",
    "<br>\n",
    "<span style=\"color:black;font-weight:400;font-size:15px;font-family:Arial\"> \n",
    "    <b>Motif Types Used:</b> <i>M1, M2, M3, M4, M5, M6, M7</i>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6dcf168",
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_motif(adjacency_matrix, motif_order, motif_type, alpha):\n",
    "    data_array = adjacency_matrix.data\n",
    "    # adjacency_matrix.data is mainly to be able to build the matrix in the binary case.\n",
    "    adjacency_matrix.data = np.ones((1, data_array.shape[0]), dtype=np.float64)[0]\n",
    "    result_B = adjacency_matrix.copy()\n",
    "    result_B = row_normal_matrix(result_B)\n",
    "    # result_B is row_normalized adjacency_matrix\n",
    "    result_C = motif_types(adjacency_matrix, motif_order, motif_type)\n",
    "    # result_C represents the normalized motif matrix \n",
    "    # The following uses the parameter alpha to fuse the two matrices\n",
    "    result_temp1 = result_B.multiply(alpha).tolil()\n",
    "    result_temp2 = result_C.multiply(1-alpha).tolil()\n",
    "    result_D = result_temp1 + result_temp2\n",
    "    MPR_adj = result_D.tocsr()\n",
    "    return MPR_adj\n",
    "\n",
    "def row_normal_matrix(mat):\n",
    "    for number in range(0, np.size(mat, 0)):\n",
    "        row_sum = mat.getrow(number).sum()\n",
    "        if row_sum == 0:\n",
    "            continue\n",
    "        else:\n",
    "            for number_col in mat.indices[mat.indptr[number]:mat.indptr[number+1]]:\n",
    "                mat[number, number_col] = mat[number, number_col] / row_sum\n",
    "    return mat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88638ced",
   "metadata": {},
   "source": [
    "### 5.3 MPR Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "109ebbd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MPR(adjacency_matrix, motif_order, motif_type, alpha):\n",
    "    mpr_adjacency_matrix = construct_motif(adjacency_matrix, motif_order, motif_type, alpha)\n",
    "    return compute_pagerank(mpr_adjacency_matrix, alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5ca5cf",
   "metadata": {},
   "source": [
    "## 6. Output \n",
    "### Test Results on original citation network:\n",
    "<span style=\"color:black;font-weight:350;font-size:16px;font-family:Arial\"> \n",
    "    <b><i>\n",
    "        <br>\n",
    "        PageRank | $\\alpha$ = 0.1 <br>\n",
    "        <br>\n",
    "        MPR | $\\alpha$ = 0.1, Motif Order = 1, Motif Type = M4\n",
    "        </i> </b>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ed88190",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_file = 'data/DBLP/citation_network.txt'\n",
    "cite_net_edges = edge_list_from_network(network_file)\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "pagerank_original_result = compute_pagerank(cite_net_adjacency_matrix, 0.1)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M4', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e936aa9d",
   "metadata": {},
   "source": [
    "## 7. Experiments | Evaluations \n",
    "\n",
    "> 1. Efficiency\n",
    "\n",
    "> 2. Robustness\n",
    "\n",
    "> 3. Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df953254",
   "metadata": {},
   "source": [
    "### 7.1 Evaluating Efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd1bcdba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98a87835baed4991ac0bc1f3d66ed58f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "cpu%:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "073e4a658c524952bd11c32ea9345753",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ram%:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Background processing cell\n",
    "import multiprocessing as mp\n",
    "from tqdm.notebook import tqdm\n",
    "from time import sleep\n",
    "import psutil\n",
    "\n",
    "def efficiency_eval_background_thread():\n",
    "    print(' ', end='', flush=True)\n",
    "    with tqdm(total=100, desc='cpu%', position=1) as cpubar, tqdm(total=100, desc='ram%', position=0) as rambar:\n",
    "        while True:\n",
    "            rambar.n=psutil.virtual_memory().percent\n",
    "            cpubar.n=psutil.cpu_percent()\n",
    "            rambar.refresh()\n",
    "            cpubar.refresh()\n",
    "            sleep(1)\n",
    "\n",
    "            \n",
    "if __name__ == '__main__':\n",
    "    p = mp.Process(target=efficiency_eval_background_thread)\n",
    "    p.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "30deeaa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979726c8",
   "metadata": {},
   "source": [
    "#### For PageRank $\\alpha = 0.1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "62b08de9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.6 s ± 272 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "pagerank_original_result = compute_pagerank(cite_net_adjacency_matrix, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dbae9292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 515.62 MiB, increment: 148.91 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "pagerank_original_result = compute_pagerank(cite_net_adjacency_matrix, 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e822b8e9",
   "metadata": {},
   "source": [
    "#### For MPR $\\alpha = 0.1 \\text{ and } M_1$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d5adf351",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.8 s ± 260 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M1', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "20ec62ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 515.32 MiB, increment: 146.47 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M1', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f71cfb",
   "metadata": {},
   "source": [
    "#### For MPR $\\alpha = 0.1 \\text{ and } M_2$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "350a5fd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.8 s ± 904 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M2', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "443c371b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 514.98 MiB, increment: 144.88 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M2', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b7d280f",
   "metadata": {},
   "source": [
    "#### For MPR $\\alpha = 0.1 \\text{ and } M_3$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "56baaca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.2 s ± 352 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M3', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "54c04ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 515.59 MiB, increment: 144.23 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M3', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4dbcb20",
   "metadata": {},
   "source": [
    "#### For MPR $\\alpha = 0.1 \\text{ and } M_4$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cd7854d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.4 s ± 264 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M4', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e740e08b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 520.19 MiB, increment: 149.47 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M4', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88fe5b35",
   "metadata": {},
   "source": [
    "#### For MPR $\\alpha = 0.1 \\text{ and } M_5$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2e83e5f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.5 s ± 315 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M5', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9c249bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 515.86 MiB, increment: 143.99 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M5', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04af44cf",
   "metadata": {},
   "source": [
    "#### For MPR $\\alpha = 0.1 \\text{ and } M_6$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6f5f3877",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.2 s ± 426 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M6', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d03a97c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 514.37 MiB, increment: 141.75 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M6', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2063020f",
   "metadata": {},
   "source": [
    "#### For MPR $\\alpha = 0.1 \\text{ and } M_7$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "84e1cf6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.4 s ± 805 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M7', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7d50bb92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 523.21 MiB, increment: 149.32 MiB\n"
     ]
    }
   ],
   "source": [
    "%%memit\n",
    "cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "MPR_result = MPR(cite_net_adjacency_matrix, 1, 'M7', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b7de4e",
   "metadata": {},
   "source": [
    "#### Plotting results - Execution Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aa9f2afa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "labels = ['PageRank'] + ['MPR M%d'%i for i in range(1,8)]\n",
    "mean_time = [15.6,23.8,24.8,24.2,24.4, 23.5, 23.2, 24.4]\n",
    "std_dev_time = [1,1,1,1,1,1,1]\n",
    "ax.bar(labels,mean_time, color=['firebrick', 'navy', 'navy','navy','navy','navy','navy','navy',])\n",
    "# ax.errorbar(labels, mean_time, yerr=std_dev_time, marker='s', mfc='red', mec='red', ms=20, mew=4)\n",
    "\n",
    "plt.title('Compute Time for Pagerank vs MPR (M1 to M7 Motifs)')\n",
    "plt.ylabel('Mean Execution Time in seconds + standard deviation')\n",
    "plt.ylim([0, 28])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c32528",
   "metadata": {},
   "source": [
    "#### Plotting results - Memory Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5b4f617c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "peak_mem = [515.62, 515.32, 514.98, 515.59, 520.19, 515.86, 514.37, 523.21]\n",
    "increment_mem = [1,1,1,1,1,1,1]\n",
    "ax.bar(labels,peak_mem, color=['firebrick', 'navy', 'navy','navy','navy','navy','navy','navy',])\n",
    "# ax.errorbar(labels, mean_time)\n",
    "\n",
    "plt.title('Memory Usage for Pagerank vs MPR (M1 to M7 Motifs)')\n",
    "plt.ylabel('Peak Memory Usage in Megabytes(MiB) + increments')\n",
    "plt.ylim([0,600])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd1a95f",
   "metadata": {},
   "source": [
    "## 7.2 Evaluating Robustness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f078d6",
   "metadata": {},
   "source": [
    "### 7.2.1 Adding extraneous nodes to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "787317de",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Running this cell is going to create the modified data again, \n",
    "and so it is suggested to not run this cell, as the data already\n",
    "exists in the data/DBLP/ directory.''' \n",
    "import random\n",
    "\n",
    "# code adapted from __main__ code in pagerank_motif_direct to instead keep a set of all node ids in the graph\n",
    "# network_file: network file path, e.g. 'data/DBLP/citation_network.txt'\n",
    "def get_ids_and_copy(network_file, out_file):\n",
    "    ids = set()\n",
    "    f = open(network_file)\n",
    "    o = open(out_file, 'w')\n",
    "    edge_count = 0\n",
    "    while True:\n",
    "        line = f.readline()\n",
    "        o.write(line)\n",
    "        if line:\n",
    "            edge_count += 1\n",
    "            line = line.replace('\\n', '')\n",
    "            line = line.split(';')\n",
    "            for i in range(len(line)):\n",
    "                ids.add(int(line[i]))\n",
    "        else:\n",
    "            break\n",
    "    return list(ids), edge_count\n",
    "    \n",
    "def append_extraneous_links(ids, edge_count, out_file, seed_val):\n",
    "    n = len(ids)\n",
    "    num_extra = int(0.01 * edge_count)\n",
    "    id_counter = max(ids)\n",
    "    out = open(out_file, 'a')\n",
    "    random.seed(seed_val)\n",
    "    for i in range(num_extra):\n",
    "        id_counter += 1\n",
    "        dst_ind = random.randint(0, n - 1) # choose a random node's index to point to\n",
    "        # point new dummy citation to the random destination\n",
    "        line = \"%d;%d\\n\" % (id_counter, ids[dst_ind])\n",
    "        out.write(line)\n",
    "        \n",
    "    \n",
    "# given a network, fill a new network with extra links\n",
    "def build_modified_network(network_file, out_file, seed_val):\n",
    "    ids, edge_count = get_ids_and_copy(network_file, out_file)\n",
    "    append_extraneous_links(ids, edge_count, out_file, seed_val)\n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    for i in range(30):\n",
    "        build_modified_network('data/DBLP/citation_network.txt', \"data/DBLP/citation_network_modified_%d.txt\" % (i), i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94db1677",
   "metadata": {},
   "source": [
    "### 7.2.2 Running PageRank and MPR on the new data\n",
    "\n",
    "> networks_list_range is a list of indices from 0 to 29 choosing which modified citation networks to use\n",
    "\n",
    "> motif is 'M1' or ... 'M7'\n",
    "\n",
    "> $\\alpha$ in 0 to 1\n",
    "\n",
    "> out_file will have the errors written in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "668e4593",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For α =  0.1\n",
      "---comparing modified network 0 has taken 97.861194 seconds\n",
      "---comparing modified network 1 has taken 154.213963 seconds\n",
      "---comparing modified network 2 has taken 211.232779 seconds\n",
      "---comparing modified network 3 has taken 272.446150 seconds\n",
      "---comparing modified network 4 has taken 327.504060 seconds\n",
      "Comparing for α = 0.100000  has taken 327.747131 seconds\n",
      "For α =  0.3\n",
      "---comparing modified network 5 has taken 94.502688 seconds\n",
      "---comparing modified network 6 has taken 151.825834 seconds\n",
      "---comparing modified network 7 has taken 208.500685 seconds\n",
      "---comparing modified network 8 has taken 264.169082 seconds\n",
      "---comparing modified network 9 has taken 320.799031 seconds\n",
      "Comparing for α = 0.300000  has taken 648.815226 seconds\n",
      "For α =  0.5\n",
      "---comparing modified network 10 has taken 96.592407 seconds\n",
      "---comparing modified network 11 has taken 154.380440 seconds\n",
      "---comparing modified network 12 has taken 211.893048 seconds\n",
      "---comparing modified network 13 has taken 269.811929 seconds\n",
      "---comparing modified network 14 has taken 327.910916 seconds\n",
      "Comparing for α = 0.500000  has taken 976.914358 seconds\n"
     ]
    }
   ],
   "source": [
    "def compare(networks_list_range, motif, alpha, out_file):\n",
    "    start = time.time()\n",
    "    \n",
    "    f = open(out_file, 'w')\n",
    "    \n",
    "    network_file = 'data/DBLP/citation_network.txt'\n",
    "    cite_net_edges = edge_list_from_network(network_file)\n",
    "    cite_net_adjacency_matrix, cite_net_node_names = get_adjacency_matrix(cite_net_edges)\n",
    "    \n",
    "    PR_old = compute_pagerank(cite_net_adjacency_matrix, alpha)\n",
    "    MPR_old = MPR(cite_net_adjacency_matrix, 1, motif, alpha)\n",
    "    \n",
    "    for i in networks_list_range:\n",
    "        network_file_modified = \"data/DBLP/citation_network_modified_%d.txt\" % (i)\n",
    "        cite_net_edges_modified = edge_list_from_network(network_file_modified)\n",
    "        cite_net_adjacency_matrix_modified, cite_net_node_names_modified = get_adjacency_matrix(cite_net_edges_modified)\n",
    "        \n",
    "        PR_new = compute_pagerank(cite_net_adjacency_matrix_modified, alpha)\n",
    "        MPR_new = MPR(cite_net_adjacency_matrix_modified, 1, motif, alpha)\n",
    "    \n",
    "        error_PR = np.sum(np.absolute(PR_old - PR_new[:len(PR_old)]))\n",
    "        error_MPR = np.sum(np.absolute(MPR_old - MPR_new[:len(MPR_old)]))\n",
    "    \n",
    "        f.write(\"%f;%f\\n\" % (error_PR, error_MPR))\n",
    "        print(\"---comparing modified network %d has taken %f seconds\" %(i,(time.time() - start)))\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    start = time.time()\n",
    "    \n",
    "    motif = 'M7'\n",
    "    \n",
    "    alpha = 0.1\n",
    "    print(\"For \\u03B1 = \", alpha)\n",
    "    compare(range(5), motif, alpha, \"output/DBLP/%s_alpha%s_errors.txt\" % (motif,alpha))\n",
    "    print(\"Comparing for \\u03B1 = %f  has taken %f seconds\" %(alpha,(time.time() - start)))\n",
    "    \n",
    "    alpha = 0.3\n",
    "    print(\"For \\u03B1 = \", alpha)\n",
    "    compare(range(5, 10), motif, alpha, \"output/DBLP/%s_alpha%s_errors.txt\" % (motif,alpha))\n",
    "    print(\"Comparing for \\u03B1 = %f  has taken %f seconds\" %(alpha,(time.time() - start)))\n",
    "    \n",
    "    alpha = 0.5\n",
    "    print(\"For \\u03B1 = \", alpha)\n",
    "    compare(range(10,15), motif, alpha, \"output/DBLP/%s_alpha%s_errors.txt\" % (motif,alpha))\n",
    "    print(\"Comparing for \\u03B1 = %f  has taken %f seconds\" %(alpha,(time.time() - start)))\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2617ed9c",
   "metadata": {},
   "source": [
    "### 7.2.3 Plotting Errors from Evaluating Robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65d97c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    f = open(\"output/DBLP/M7_alpha0.1_errors.txt\")\n",
    "    pr_1 = []\n",
    "    mpr_1 = []\n",
    "    while True:\n",
    "        line = f.readline()\n",
    "        if line:\n",
    "            line = line.strip()\n",
    "            line = line.split(';')\n",
    "            pr_1.append(float(line[0]))\n",
    "            mpr_1.append(float(line[1]))\n",
    "        else:\n",
    "            break\n",
    "    f.close()\n",
    "        \n",
    "    f = open(\"output/DBLP/M7_alpha0.3_errors.txt\")\n",
    "    pr_3 = []\n",
    "    mpr_3 = []\n",
    "    while True:\n",
    "        line = f.readline()\n",
    "        if line:\n",
    "            line = line.strip()\n",
    "            line = line.split(';')\n",
    "            pr_3.append(float(line[0]))\n",
    "            mpr_3.append(float(line[1]))\n",
    "        else:\n",
    "            break\n",
    "    f.close()\n",
    "        \n",
    "    f = open(\"output/DBLP/M7_alpha0.5_errors.txt\")\n",
    "    pr_5 = []\n",
    "    mpr_5 = []\n",
    "    while True:\n",
    "        line = f.readline()\n",
    "        if line:\n",
    "            line = line.strip()\n",
    "            line = line.split(';')\n",
    "            pr_5.append(float(line[0]))\n",
    "            mpr_5.append(float(line[1]))\n",
    "        else:\n",
    "            break\n",
    "    f.close()\n",
    "    \n",
    "    fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (10,6))\n",
    "    \n",
    "    axes[0].plot(range(5), pr_1, label = \"PR\")\n",
    "    axes[0].plot(range(5), mpr_1, label = \"MPR\")\n",
    "    axes[0].set_ylim(0.04, 0.2)\n",
    "    axes[0].set_xlabel(\"modified citation network #\")\n",
    "    axes[0].set_ylabel(\"error\")\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title(\"alpha = 0.1\")\n",
    "    \n",
    "    axes[1].plot(range(5,10), pr_3, label = \"PR\")\n",
    "    axes[1].plot(range(5,10), mpr_3, label = \"MPR\")\n",
    "    axes[1].set_ylim(0.04, 0.2)\n",
    "    axes[1].set_xlabel(\"modified citation network #\")\n",
    "    axes[1].set_ylabel(\"error\")\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title(\"alpha = 0.3\")\n",
    "    \n",
    "    axes[2].plot(range(10,15), pr_5, label = \"PR\")\n",
    "    axes[2].plot(range(10,15), mpr_5, label = \"MPR\")\n",
    "    axes[2].set_ylim(0.04, 0.2)\n",
    "    axes[2].set_xlabel(\"modified citation network #\")\n",
    "    axes[2].set_ylabel(\"error\")\n",
    "    axes[2].legend()\n",
    "    axes[2].set_title(\"alpha = 0.5\")\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c7c5701",
   "metadata": {},
   "source": [
    "## 7.3 Evaluating Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32cebd5",
   "metadata": {},
   "source": [
    "### 7.3.1 Normalized Discounted Cumulative Gain $nDCG$\n",
    "\n",
    "> Adapted from <i>ndcg_DBLP.py</i> from <i>H.Zhao Et.Al</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "238fcb33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "\n",
    "def read_top(txt_name, dict, K):\n",
    "    f = open(txt_name)\n",
    "    count = 0\n",
    "    result_array = []\n",
    "    name_array = []\n",
    "    while True:\n",
    "        line = f.readline()\n",
    "        if count >= K:\n",
    "            break\n",
    "        if line:\n",
    "            line = line.strip()\n",
    "            line = line.split(';')\n",
    "            name = line[1]\n",
    "            score = dict[name]\n",
    "            if score < 30:\n",
    "                continue\n",
    "            result_array.append(score)\n",
    "            name_array.append(name)\n",
    "            count += 1\n",
    "        else:\n",
    "            break\n",
    "    return result_array, name_array\n",
    "\n",
    "\n",
    "def construct(txt_name):\n",
    "    f = open(txt_name)\n",
    "    user = {}\n",
    "    count = 0\n",
    "    while True:\n",
    "        line = f.readline()\n",
    "        if line:\n",
    "            line = line.strip()\n",
    "            line = line.split(';')\n",
    "            name = line[0]\n",
    "            score = int(line[1])\n",
    "            user[name] = score\n",
    "            count += 1\n",
    "        else:\n",
    "            break\n",
    "    return user\n",
    "\n",
    "\n",
    "def get_ndcg(result1, result2):\n",
    "    sum1 = 0\n",
    "    sum2 = 0\n",
    "    for i in range(len(result1)):\n",
    "        sum1 += result1[i] * 1.0 / (np.log2(i+2))\n",
    "        sum2 += result2[i] * 1.0 / (np.log2(i+2))\n",
    "    return sum2 * 1.0 / sum1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae81daad",
   "metadata": {},
   "source": [
    "### 7.4.2 Compute Normalized Discounted Cumulative Gain $nDCG$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af37a3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generalizes __main__ code from ndcg_DBLP for our experiments\n",
    "def compute_ndcg(results_file, K):\n",
    "    txt_name3 = 'data/DBLP/h_index_all.txt'\n",
    "    dict_author = construct(txt_name3)\n",
    "    txt_name = results_file\n",
    "    rank_value = K\n",
    "    rank_array, name_array = read_top(txt_name, dict_author, rank_value)\n",
    "    result = {}\n",
    "    for i in range(len(rank_array)):\n",
    "        score = dict_author[name_array[i]]\n",
    "        result[name_array[i]] = score\n",
    "    result_array = []\n",
    "    dict = sorted(result.items(), key=lambda item: item[1], reverse=True)\n",
    "    for i in range(len(dict)):\n",
    "        result_array.append(dict[i][1])\n",
    "\n",
    "    score = get_ndcg(result_array, rank_array)\n",
    "    return score\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # for top 10, top 50, top 250, and top 500\n",
    "    K_list = [50,100,250,500,1000]\n",
    "    out = open(\"output/DBLP/ndcg_PR.txt\", 'w')\n",
    "    \n",
    "    for K in K_list:\n",
    "        score = compute_ndcg(\"output/DBLP/result_PR.txt\", K)\n",
    "        out.write(\"%d;%f\\n\" % (K,score))\n",
    "    out.close()\n",
    "    \n",
    "    for i in range(1,8):\n",
    "        motif = \"M%d\" % (i)\n",
    "        out = open(\"output/DBLP/ndcg_%s.txt\" % (motif), 'w')\n",
    "        for K in K_list:\n",
    "            score = compute_ndcg(\"output/DBLP/result_%s.txt\" % (motif), K)\n",
    "            out.write(\"%d;%f\\n\" % (K,score))\n",
    "        out.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25fadcfc",
   "metadata": {},
   "source": [
    "### 7.4.3 Plotting $nDCG$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c521a40b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    f = open(\"output/DBLP/ndcg_PR.txt\")\n",
    "    \n",
    "    K_list = []\n",
    "    ndcg_PR = []\n",
    "    while True:\n",
    "        line = f.readline()\n",
    "        if line:\n",
    "            line = line.strip()\n",
    "            line = line.split(';')\n",
    "            K = line[0]\n",
    "            val = float(line[1])\n",
    "            \n",
    "            K_list.append(K)\n",
    "            ndcg_PR.append(val)\n",
    "        else:\n",
    "            break\n",
    "    f.close()\n",
    "        \n",
    "    ndcg_M = []\n",
    "    for i in range(1,8):\n",
    "        ndcg_M_i = []\n",
    "        f = open(\"output/DBLP/ndcg_M%s.txt\" %(i))\n",
    "        while True:\n",
    "            line = f.readline()\n",
    "            if line:\n",
    "                line = line.strip()\n",
    "                line = line.split(';')\n",
    "                K = line[0]\n",
    "                val = float(line[1])\n",
    "                \n",
    "                ndcg_M_i.append(val)\n",
    "            else:\n",
    "                break\n",
    "        f.close()\n",
    "        ndcg_M.append(ndcg_M_i)\n",
    "    \n",
    "    fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (10,6))\n",
    "    \n",
    "    for i in range(1,8):\n",
    "        axes[0].plot(K_list, ndcg_M[i-1], label = \"M%s\" % (i))\n",
    "    \n",
    "    axes[0].set_ylim(0.885, 0.965)\n",
    "    axes[0].set_xlabel(\"top K values used\")\n",
    "    axes[0].set_ylabel(\"NDCG\")\n",
    "    axes[0].legend()\n",
    "    \n",
    "    for i in range(1,8):\n",
    "        axes[1].plot(K_list, ndcg_M[i-1], label = \"M%s\" % (i), color = 'black')\n",
    "    axes[1].plot(K_list, ndcg_PR, label = \"PR\", color = 'blue')\n",
    "    axes[1].set_ylim(0.885, 0.965)\n",
    "    axes[1].set_xlabel(\"top K values used\")\n",
    "    axes[1].set_ylabel(\"NDCG\")\n",
    "    axes[1].legend()\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DSC_NLA_CondaEnv",
   "language": "python",
   "name": "dsc_nla_condaenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}