WL_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tp07h2rVr6NixIzJWfwE+z7WbsAEI7z9ZKMtN2J6K5pEJBbLCcByHvLw8yUfIdwoPD8eq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x219414df518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------init-----------\n",
      "{1: '1', 2: '1', 3: '1', 4: '1', 5: '1', 6: '1'}\n",
      "{1: '1', 2: '1', 3: '1', 4: '1', 5: '1', 6: '1'}\n",
      "-------iteration 1 -------\n",
      "[2, 2, 3, 4, 4, 5]\n",
      "[2, 2, 3, 4, 4, 5]\n",
      "Maybe isomorphism!!!\n",
      "-------iteration 2 -------\n",
      "[7, 7, 8, 11, 11, 12]\n",
      "[6, 7, 9, 10, 11, 13]\n",
      "Not isomorphism!!!\n",
      "-------iteration 3 -------\n",
      "[16, 16, 17, 20, 20, 22]\n",
      "[14, 15, 18, 19, 21, 23]\n",
      "Not isomorphism!!!\n",
      "-------iteration 4 -------\n",
      "[26, 26, 27, 30, 30, 32]\n",
      "[24, 25, 28, 29, 31, 33]\n",
      "Not isomorphism!!!\n",
      "-------iteration 5 -------\n",
      "[36, 36, 37, 40, 40, 42]\n",
      "[34, 35, 38, 39, 41, 43]\n",
      "Not isomorphism!!!\n",
      "-------iteration 6 -------\n",
      "[46, 46, 47, 50, 50, 52]\n",
      "[44, 45, 48, 49, 51, 53]\n",
      "Not isomorphism!!!\n",
      "-------iteration 7 -------\n",
      "[56, 56, 57, 60, 60, 62]\n",
      "[54, 55, 58, 59, 61, 63]\n",
      "Not isomorphism!!!\n",
      "-------iteration 8 -------\n",
      "[66, 66, 67, 70, 70, 72]\n",
      "[64, 65, 68, 69, 71, 73]\n",
      "Not isomorphism!!!\n",
      "-------iteration 9 -------\n",
      "[76, 76, 77, 80, 80, 82]\n",
      "[74, 75, 78, 79, 81, 83]\n",
      "Not isomorphism!!!\n",
      "-------iteration 10 -------\n",
      "[86, 86, 87, 90, 90, 92]\n",
      "[84, 85, 88, 89, 91, 93]\n",
      "Not isomorphism!!!\n",
      "-------iteration 11 -------\n",
      "[96, 96, 97, 100, 100, 102]\n",
      "[94, 95, 98, 99, 101, 103]\n",
      "Not isomorphism!!!\n",
      "-------iteration 12 -------\n",
      "[104, 104, 106, 110, 110, 111]\n",
      "[105, 107, 108, 109, 112, 113]\n",
      "Not isomorphism!!!\n",
      "-------iteration 13 -------\n",
      "[114, 114, 116, 120, 120, 121]\n",
      "[115, 117, 118, 119, 122, 123]\n",
      "Not isomorphism!!!\n",
      "-------iteration 14 -------\n",
      "[124, 124, 126, 130, 130, 131]\n",
      "[125, 127, 128, 129, 132, 133]\n",
      "Not isomorphism!!!\n",
      "-------iteration 15 -------\n",
      "[134, 134, 136, 140, 140, 141]\n",
      "[135, 137, 138, 139, 142, 143]\n",
      "Not isomorphism!!!\n",
      "-------iteration 16 -------\n",
      "[144, 144, 146, 150, 150, 151]\n",
      "[145, 147, 148, 149, 152, 153]\n",
      "Not isomorphism!!!\n",
      "-------iteration 17 -------\n",
      "[154, 154, 156, 160, 160, 161]\n",
      "[155, 157, 158, 159, 162, 163]\n",
      "Not isomorphism!!!\n",
      "-------iteration 18 -------\n",
      "[164, 164, 166, 170, 170, 171]\n",
      "[165, 167, 168, 169, 172, 173]\n",
      "Not isomorphism!!!\n",
      "-------iteration 19 -------\n",
      "[174, 174, 176, 180, 180, 181]\n",
      "[175, 177, 178, 179, 182, 183]\n",
      "Not isomorphism!!!\n",
      "-------iteration 20 -------\n",
      "[184, 184, 186, 190, 190, 191]\n",
      "[185, 187, 188, 189, 192, 193]\n",
      "Not isomorphism!!!\n",
      "-------iteration 21 -------\n",
      "[194, 194, 196, 200, 200, 201]\n",
      "[195, 197, 198, 199, 202, 203]\n",
      "Not isomorphism!!!\n",
      "-------iteration 22 -------\n",
      "[204, 204, 206, 210, 210, 211]\n",
      "[205, 207, 208, 209, 212, 213]\n",
      "Not isomorphism!!!\n",
      "-------iteration 23 -------\n",
      "[214, 214, 216, 220, 220, 221]\n",
      "[215, 217, 218, 219, 222, 223]\n",
      "Not isomorphism!!!\n",
      "-------iteration 24 -------\n",
      "[224, 224, 226, 230, 230, 231]\n",
      "[225, 227, 228, 229, 232, 233]\n",
      "Not isomorphism!!!\n",
      "-------iteration 25 -------\n",
      "[234, 234, 236, 240, 240, 241]\n",
      "[235, 237, 238, 239, 242, 243]\n",
      "Not isomorphism!!!\n",
      "-------iteration 26 -------\n",
      "[244, 244, 246, 250, 250, 251]\n",
      "[245, 247, 248, 249, 252, 253]\n",
      "Not isomorphism!!!\n",
      "-------iteration 27 -------\n",
      "[254, 254, 256, 260, 260, 261]\n",
      "[255, 257, 258, 259, 262, 263]\n",
      "Not isomorphism!!!\n",
      "-------iteration 28 -------\n",
      "[264, 264, 266, 270, 270, 271]\n",
      "[265, 267, 268, 269, 272, 273]\n",
      "Not isomorphism!!!\n",
      "-------iteration 29 -------\n",
      "[274, 274, 276, 280, 280, 281]\n",
      "[275, 277, 278, 279, 282, 283]\n",
      "Not isomorphism!!!\n",
      "-------iteration 30 -------\n",
      "[284, 284, 286, 290, 290, 291]\n",
      "[285, 287, 288, 289, 292, 293]\n",
      "Not isomorphism!!!\n"
     ]
    },
    {
     "data": {
      "image/png": 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kLhaMSb6tf06VZ9QyN27cYNSoUVSuXPnxTc2///47b731ltHXItnCQwbnr9qT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\n6AQSZIIgCJ3wP/mfMksgBYVlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2193e0f67b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------init-----------\n",
      "{1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6'}\n",
      "{1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6'}\n",
      "-------iteration 1 -------\n",
      "[8, 10, 12, 13, 15, 17]\n",
      "[7, 9, 11, 14, 16, 18]\n",
      "Not isomorphism!!!\n",
      "-------iteration 2 -------\n",
      "[19, 21, 22, 24, 26, 29]\n",
      "[20, 23, 25, 27, 28, 30]\n",
      "Not isomorphism!!!\n",
      "-------iteration 3 -------\n",
      "[31, 33, 34, 36, 38, 41]\n",
      "[32, 35, 37, 39, 40, 42]\n",
      "Not isomorphism!!!\n",
      "-------iteration 4 -------\n",
      "[43, 45, 46, 48, 50, 53]\n",
      "[44, 47, 49, 51, 52, 54]\n",
      "Not isomorphism!!!\n",
      "-------iteration 5 -------\n",
      "[55, 57, 58, 60, 62, 65]\n",
      "[56, 59, 61, 63, 64, 66]\n",
      "Not isomorphism!!!\n",
      "-------iteration 6 -------\n",
      "[67, 69, 70, 72, 74, 77]\n",
      "[68, 71, 73, 75, 76, 78]\n",
      "Not isomorphism!!!\n",
      "-------iteration 7 -------\n",
      "[79, 81, 82, 84, 86, 89]\n",
      "[80, 83, 85, 87, 88, 90]\n",
      "Not isomorphism!!!\n",
      "-------iteration 8 -------\n",
      "[91, 93, 94, 96, 98, 101]\n",
      "[92, 95, 97, 99, 100, 102]\n",
      "Not isomorphism!!!\n",
      "-------iteration 9 -------\n",
      "[104, 106, 108, 109, 111, 113]\n",
      "[103, 105, 107, 110, 112, 114]\n",
      "Not isomorphism!!!\n",
      "-------iteration 10 -------\n",
      "[116, 118, 120, 121, 123, 125]\n",
      "[115, 117, 119, 122, 124, 126]\n",
      "Not isomorphism!!!\n",
      "-------iteration 11 -------\n",
      "[128, 130, 132, 133, 135, 137]\n",
      "[127, 129, 131, 134, 136, 138]\n",
      "Not isomorphism!!!\n",
      "-------iteration 12 -------\n",
      "[140, 142, 144, 145, 147, 149]\n",
      "[139, 141, 143, 146, 148, 150]\n",
      "Not isomorphism!!!\n",
      "-------iteration 13 -------\n",
      "[152, 154, 156, 157, 159, 161]\n",
      "[151, 153, 155, 158, 160, 162]\n",
      "Not isomorphism!!!\n",
      "-------iteration 14 -------\n",
      "[164, 166, 168, 169, 171, 173]\n",
      "[163, 165, 167, 170, 172, 174]\n",
      "Not isomorphism!!!\n",
      "-------iteration 15 -------\n",
      "[176, 178, 180, 181, 183, 185]\n",
      "[175, 177, 179, 182, 184, 186]\n",
      "Not isomorphism!!!\n",
      "-------iteration 16 -------\n",
      "[188, 190, 192, 193, 195, 197]\n",
      "[187, 189, 191, 194, 196, 198]\n",
      "Not isomorphism!!!\n",
      "-------iteration 17 -------\n",
      "[200, 202, 204, 205, 207, 209]\n",
      "[199, 201, 203, 206, 208, 210]\n",
      "Not isomorphism!!!\n",
      "-------iteration 18 -------\n",
      "[212, 214, 216, 217, 219, 221]\n",
      "[211, 213, 215, 218, 220, 222]\n",
      "Not isomorphism!!!\n",
      "-------iteration 19 -------\n",
      "[224, 226, 228, 229, 231, 233]\n",
      "[223, 225, 227, 230, 232, 234]\n",
      "Not isomorphism!!!\n",
      "-------iteration 20 -------\n",
      "[236, 238, 240, 241, 243, 245]\n",
      "[235, 237, 239, 242, 244, 246]\n",
      "Not isomorphism!!!\n",
      "-------iteration 21 -------\n",
      "[248, 250, 252, 253, 255, 257]\n",
      "[247, 249, 251, 254, 256, 258]\n",
      "Not isomorphism!!!\n",
      "-------iteration 22 -------\n",
      "[260, 262, 264, 265, 267, 269]\n",
      "[259, 261, 263, 266, 268, 270]\n",
      "Not isomorphism!!!\n",
      "-------iteration 23 -------\n",
      "[272, 274, 276, 277, 279, 281]\n",
      "[271, 273, 275, 278, 280, 282]\n",
      "Not isomorphism!!!\n",
      "-------iteration 24 -------\n",
      "[284, 286, 288, 289, 291, 293]\n",
      "[283, 285, 287, 290, 292, 294]\n",
      "Not isomorphism!!!\n",
      "-------iteration 25 -------\n",
      "[296, 298, 300, 301, 303, 305]\n",
      "[295, 297, 299, 302, 304, 306]\n",
      "Not isomorphism!!!\n",
      "-------iteration 26 -------\n",
      "[308, 310, 312, 313, 315, 317]\n",
      "[307, 309, 311, 314, 316, 318]\n",
      "Not isomorphism!!!\n",
      "-------iteration 27 -------\n",
      "[320, 322, 324, 325, 327, 329]\n",
      "[319, 321, 323, 326, 328, 330]\n",
      "Not isomorphism!!!\n",
      "-------iteration 28 -------\n",
      "[332, 334, 336, 337, 339, 341]\n",
      "[331, 333, 335, 338, 340, 342]\n",
      "Not isomorphism!!!\n",
      "-------iteration 29 -------\n",
      "[344, 346, 348, 349, 351, 353]\n",
      "[343, 345, 347, 350, 352, 354]\n",
      "Not isomorphism!!!\n",
      "-------iteration 30 -------\n",
      "[356, 358, 360, 361, 363, 365]\n",
      "[355, 357, 359, 362, 364, 366]\n",
      "Not isomorphism!!!\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from builtins import str\n",
    "    \n",
    "def draw_graphs(G,G1):\n",
    "    pos = nx.spring_layout(G)\n",
    "    pos1 = nx.spring_layout(G1) \n",
    "    plt.subplot(121)\n",
    "    nx.draw(G, pos, with_labels=True, node_color='pink', node_size=500)\n",
    "    plt.subplot(122)\n",
    "    nx.draw(G1, pos1, with_labels=True, node_size=500)\n",
    "    plt.show()\n",
    "\n",
    "def multiset_define(G): #复合集的生成与排序拼接,复合集value为邻居节点排序后标签\n",
    "    multiset = {}\n",
    "    lab = nx.get_node_attributes(G,'label')\n",
    "    for u in G.nodes():  \n",
    "        list = []\n",
    "        a = nx.all_neighbors(G,u)     \n",
    "        for v in a:\n",
    "            list.append(lab[v])    \n",
    "        list.sort()\n",
    "        list_new = [str(x) for x in list]\n",
    "        multiset[u] = ''.join(list_new)\n",
    "    return multiset\n",
    "\n",
    "def multiset_join(G,dict):    #把v上一轮的标签作为前缀，添加到s里\n",
    "    node_label = nx.get_node_attributes(G,'label')\n",
    "    for u in dict.keys():\n",
    "        dict[u] = node_label[u] + dict[u]    \n",
    "    return dict\n",
    "\n",
    "#标签压缩，把G和G1的所有复合集升序排列，且按顺序标号\n",
    "def label_compression(dict1,dict2,G,G1): \n",
    "    node_label = nx.get_node_attributes(G,'label')\n",
    "    node_label1 = nx.get_node_attributes(G1,'label')\n",
    "    result = map(int,node_label.values() )\n",
    "    result1 = map(int, node_label1.values())\n",
    "    n = max(max(result),max(result1)) + 1  #重标签起始数\n",
    "    strings = [] #存放两个图标签集组合\n",
    "    \n",
    "    for v in dict1.values():   #将两个图的标签集集合起来\n",
    "        strings.append(v)\n",
    "    for u in dict2.values():\n",
    "        strings.append(u)  \n",
    "    str_remove = []   #存放去重以后的字符串列表\n",
    "    for i in strings:\n",
    "        if i not in str_remove:\n",
    "            str_remove.append(i)  \n",
    "    str_remove.sort()  #排序后的字符串列表     \n",
    "    map_dict = {}     #给新标签顺次标号\n",
    "    for m in range(len(str_remove)):\n",
    "        map_dict[str_remove[m]] = str(n)\n",
    "        n += 1 \n",
    "    return map_dict\n",
    "\n",
    "def label_update(dic,map_dict):\n",
    "    for  u in dic.keys():\n",
    "        for v in map_dict.keys():\n",
    "            if(dic[u] == v):\n",
    "                dic[u] = map_dict[v]\n",
    "    return dic\n",
    "\n",
    "def WL_test(dict1,dict2,G,G1): \n",
    "    i = 1\n",
    "    list1 = dict1   #初始化标签复合集\n",
    "    list2 = dict2\n",
    "    print(\"-----------init-----------\")\n",
    "    print(list1)\n",
    "    print(list2)\n",
    "    while i<= 30:   #第i轮生成的标签集一样时，两图可能同构    \n",
    "        print(\"-------iteration\", i,\"-------\")\n",
    "        list1 = multiset_define(G)\n",
    "        list2 = multiset_define(G1)\n",
    "        dict1 = multiset_join(G,list1)\n",
    "        dict2 = multiset_join(G1,list2) #复合集排序，元素是邻居在上一轮迭代生成的标签集排序后拼接成的字符串\n",
    "        map_dict = label_compression(dict1,dict2,G,G1) #标签压缩，生成映射字典\n",
    "        list1 = label_update(dict1, map_dict)\n",
    "        list2 = label_update(dict2, map_dict) #更新标签\n",
    "        nx.set_node_attributes(G,list1,'label')\n",
    "        nx.set_node_attributes(G1,list2,'label')\n",
    "        result = sorted(map(int,list1.values()))\n",
    "        result1 = sorted(map(int, list2.values()))\n",
    "        print(result)\n",
    "        print(result1)\n",
    "        if result != result1:\n",
    "            print(\"Not isomorphism!!!\")\n",
    "        else:\n",
    "            print(\"Maybe isomorphism!!!\")  \n",
    "        i += 1\n",
    "        \n",
    "\n",
    "##################################################################\n",
    "# draw_graphs_test()\n",
    "G = nx.MultiGraph()   #有多重边无向图\n",
    "G.add_node(1,label='1')  #添加节点\n",
    "G.add_node(2,label='1')\n",
    "G.add_node(3,label='1')\n",
    "G.add_node(4,label='1')\n",
    "G.add_node(5,label='1')\n",
    "G.add_node(6,label='1')\n",
    "edges = [(1,5),(2,5),(4,5),(3,4),(3,6),(4,6),(5,6)]\n",
    "G.add_edges_from(edges)\n",
    "\n",
    "G1 = nx.MultiGraph()   #有多重边无向图\n",
    "G1.add_node(1,label='1')  #添加节点\n",
    "G1.add_node(2,label='1')\n",
    "G1.add_node(3,label='1')\n",
    "G1.add_node(4,label='1')\n",
    "G1.add_node(5,label='1')\n",
    "G1.add_node(6,label='1')\n",
    "edges1 = [(1,5),(2,4),(4,5),(3,5),(3,6),(4,6),(5,6)]\n",
    "G1.add_edges_from(edges1)\n",
    "\n",
    "pos = nx.spring_layout(G)\n",
    "plt.subplot(121)\n",
    "node_labels = nx.get_node_attributes(G, 'label')\n",
    "nx.draw(G, pos, with_labels=False, node_color='pink', node_size=500)\n",
    "nx.draw_networkx_labels(G, pos, labels = node_labels)\n",
    "\n",
    "pos1 = nx.spring_layout(G1)\n",
    "plt.subplot(122)\n",
    "node_labels1 = nx.get_node_attributes(G1, 'label')\n",
    "nx.draw(G1, pos1, with_labels=False, node_size=500)\n",
    "nx.draw_networkx_labels(G1, pos1, labels = node_labels1)\n",
    "plt.show()\n",
    "\n",
    "WL_test(node_labels,node_labels1,G,G1)\n",
    "\n",
    "##################################################################\n",
    "Ga = nx.MultiGraph()   #有多重边无向图\n",
    "Ga.add_node(1,label='1')  #添加节点\n",
    "Ga.add_node(2,label='2')\n",
    "Ga.add_node(3,label='3')\n",
    "Ga.add_node(4,label='4')\n",
    "Ga.add_node(5,label='5')\n",
    "Ga.add_node(6,label='6')\n",
    "edgesa = [(1,4),(1,5),(1,6),(2,4),(2,5),(2,6),(3,4),(3,5),(3,6)]\n",
    "Ga.add_edges_from(edgesa)\n",
    "\n",
    "Gb = nx.MultiGraph()   #有多重边无向图\n",
    "Gb.add_node(1,label='1')  #添加节点\n",
    "Gb.add_node(2,label='2')\n",
    "Gb.add_node(3,label='3')\n",
    "Gb.add_node(4,label='4')\n",
    "Gb.add_node(5,label='5')\n",
    "Gb.add_node(6,label='6')\n",
    "edgesb = [(1,2),(2,3),(3,4),(4,5),(5,6),(6,1),(1,4),(2,5),(3,6)]\n",
    "Gb.add_edges_from(edgesb)\n",
    "\n",
    "posa = nx.spring_layout(Ga)\n",
    "plt.subplot(121)\n",
    "node_labelsa = nx.get_node_attributes(Ga, 'label')\n",
    "nx.draw(Ga, posa, with_labels=False, node_color='pink', node_size=500)\n",
    "nx.draw_networkx_labels(Ga, posa, labels = node_labelsa)\n",
    "\n",
    "posb = nx.spring_layout(Gb)\n",
    "plt.subplot(122)\n",
    "node_labelsb = nx.get_node_attributes(Gb, 'label')\n",
    "nx.draw(Gb, posb, with_labels=False, node_size=500)\n",
    "nx.draw_networkx_labels(Gb, posb, labels = node_labelsb)\n",
    "plt.show()\n",
    "\n",
    "WL_test(node_labelsa,node_labelsb,Ga,Gb)\n"
   ]
  }
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