{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Node equivalence with Node2Vec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Node2Vec is an algorithm designed for detecting structural equivalence and segments of a graph.\n", "\n", "In this notebook, the Leviticus-network will be explored with Node2Vec in a progression of complexity, according to the following criteria:\n", "1. The network as a directed but unweighted graph\n", "2. The network as a multiple directed graph (weights)\n", "3. The network as a multiple directed graph (weighted by average agency values)\n", "\n", "**Other tutorials**\n", "* https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef\n", "* https://medium.com/neo4j/machine-learning-on-graphs-fca6eeb8f1d1\n", "* https://www.kaggle.com/ferdzso/knowledge-graph-analysis-with-node2vec\n", "* https://www.kaggle.com/pierremegret/gensim-word2vec-tutorial" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import collections\n", "\n", "import networkx as nx\n", "from node2vec import Node2Vec\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.cluster import KMeans\n", "from sklearn.manifold import MDS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SourceSource_agencySource_phTargetTarget_agencyTarget_phLabelWeightClause
0Aaron's_sons5690343YHWH0690347swing25440323
1YHWH5690383Moses-1690384speak36440335
2Israelites5690397YHWH-1690399approach36440341
3YHWH5690402Moses-1690403speak36440342
4Israelites5690415YHWH-1690417approach36440347
\n", "
" ], "text/plain": [ " Source Source_agency Source_ph Target Target_agency Target_ph \\\n", "0 Aaron's_sons 5 690343 YHWH 0 690347 \n", "1 YHWH 5 690383 Moses -1 690384 \n", "2 Israelites 5 690397 YHWH -1 690399 \n", "3 YHWH 5 690402 Moses -1 690403 \n", "4 Israelites 5 690415 YHWH -1 690417 \n", "\n", " Label Weight Clause \n", "0 swing 25 440323 \n", "1 speak 36 440335 \n", "2 approach 36 440341 \n", "3 speak 36 440342 \n", "4 approach 36 440347 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_excel('Lev17-26.edges.Static.xlsx')\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Simple, directed graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "G = nx.DiGraph()\n", "\n", "for n, row in data.iterrows():\n", " G.add_edge(row.Source, row.Target)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize = (15,15))\n", "\n", "nx.draw(\n", " G,\n", " pos=nx.spring_layout(G),\n", " with_labels=True,\n", " edge_color='grey'\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define a few global measures to account for in all examples:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dim = 16\n", "wl = 4\n", "nw = 150\n", "p=1\n", "q=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "node2vec = Node2Vec(G, dimensions=dim, walk_length=wl, num_walks=nw, p=p, q=q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definitions:\n", "* window (size) = the maximum distance between the current and the predicted node in the vector\n", "* min_count = ignores all words with total absolute frequency lower than this" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = node2vec.fit(window=6, min_count=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.wv.save_word2vec_format('node2vec/simple_directed')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clustering embeddings" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def getEmbeddings(file, nodes):\n", " with open(file) as f:\n", " mylist = [line.rstrip('\\n') for line in f]\n", "\n", " embeddings_dict = collections.defaultdict()\n", " for l in mylist[1:]:\n", " vector = l.split()\n", " embeddings_dict[vector[0]] = vector[1:]\n", "\n", " \"\"\"Extract representations from the node2vec model\"\"\"\n", " embeddings = [embeddings_dict[str(n)] for n in nodes]\n", " embeddings = np.array(embeddings)\n", " return embeddings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### How many clusters?\n", "\n", "The more clusters, the less the distance is between the centroid and its members. This is measured with WCSS (\"within-cluster sum of squares\")." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Christian\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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G4EH1E4hIQuUtCMxsupn9zcweM7MVZnZZH9OcbmYtZrY03D6dr3r6c/S0GipLi3Q+gYgkVnEe590NXO7uS8xsHLDYzP7s7o/mTHePu5+XxzoGVFKUYt6sCeowFpHEytsegbtvcPcl4f524DGgPl/LOxBNDWme2rSDzdvVTyAiyTMifQRmNgs4DljYx+gmM3vYzO4wsyP6ef6lZrbIzBZt3rx52Otraoz6CRau0l6BiCRP3oPAzKqBm4EPu3trzuglwEx3Pwb4DnBrX/Nw96vdfZ67z5s4ceKw13jk1PFUlxWreUhEEimvQWBmJUQh8At3vyV3vLu3uvuOcP92oMTM6vJZU1+Ki1KcOOsgdRiLSCLl86ghA64BHnP3b/QzzeQwHWZ2Uqgnlq1xU2OalZvbeK61I47Fi4jEJp9HDZ0CvBVYZmZLw7BPAjMA3P0q4HXA+8ysG2gHLnJ3z2NN/WpqiHZEFqzcygXHFmSftohIXuQtCNz9XsAGmeZK4Mp81bAv5k4dz7jyYgWBiCRO4s8szihKGSfP1vkEIpI8CoIs8xvSrN66kw0t7XGXIiIyYhQEWTLnE+hqpCKSJAqCLIdPHk9NRYmah0QkURQEWVKZfgLtEYhIgigIcjQ1plmzrZ21z++MuxQRkRGhIMixp59Av08gIsmgIMjxoknjmFBVqn4CEUkMBUGOTD/BgpVbiekkZxGREaUg6ENTY5p1ze2sfV7nE4jI2Kcg6ENT+B1jNQ+JSBIoCPowZ1I1ddWlOoxURBJBQdAHM+PkhjQP/FP9BCIy9ikI+tHUkGZjawfPbNX5BCIytikI+pE5n0DNQyIy1ikI+tFQV8WkcWXqMBaRMU9B0A8zY35Dmgd0PoGIjHEKggE0NabZvL2TlVva4i5FRCRvFAQD0PkEIpIECoIBzExXMqWmXB3GIjKmKQgGkOknWKh+AhEZwxQEg2hqSLNlxy6e3rQj7lJERPJCQTAInU8gImOdgmAQ0w6qoL62Qh3GIjJmKQgGkeknWLByK7296icQkbFHQTAETY1pnt/ZxZObtsddiojIsFMQDMH8hgmAzicQkbFJQTAE0w6qZPoE9ROIyNikIBiipoY0C1dtUz+BiIw5eQsCM5tuZn8zs8fMbIWZXdbHNGZmV5jZ02b2iJkdn696DlRTY5qW9i4e29gadykiIsMqn3sE3cDl7n44MB94v5nNzZnmbOCQcLsU+H4e6zkg83XdIREZo/IWBO6+wd2XhPvbgceA+pzJLgCu88gCoNbMpuSrpgMxpaaCWelKFujEMhEZY0akj8DMZgHHAQtzRtUDa7Ier+WFYYGZXWpmi8xs0ebNm/NW52CaGqN+gh71E4jIGJL3IDCzauBm4MPuntvAbn085QVbWXe/2t3nufu8iRMn5qPMIZnfkGZ7RzePrlc/gYiMHXkNAjMrIQqBX7j7LX1MshaYnvV4GrA+nzUdiN2/T7ByS8yViIgMn3weNWTANcBj7v6Nfib7LfC2cPTQfKDF3Tfkq6YDNWl8OQ0Tq9RhLCJjSnEe530K8FZgmZktDcM+CcwAcPergNuBc4CngZ3AO/JYz7Boakhz29L1dPf0Ulyk0zBEZPTLWxC4+7303QeQPY0D789XDfnQ1JjmFwufZfn6Vo6dXht3OSIiB0xfafeRzicQkbFGQbCP6qrLOGRStX6oRkTGDAXBfmhqTLNo9Ta6enrjLkVE5IApCPZDU0Oanbt6eGRtS9yliIgcMAXBfjg59BPochMiMhYoCPbDhKpSDps8Th3GIjImKAj20/yGNIue2UZnd0/cpYiIHBAFwX5qakzT0dWrfgIRGfUUBPvp5NkTMNP5BCIy+ikI9lNtZSmHTx6vIBCRUU9BcACaGtMsefZ5OrrUTyAio5eC4AA0NaTp7O5l6ZrmuEsREdlvCoIDcOLsCaTUTyAio5yC4ADUVJRwxNQaXXdIREY1BcEBampMs/TZZvUTiMiopSA4QE0NaXb19LLkmefjLkVEZL8MGARmdqKZTc56/DYzu83MrjCzCfkvr/DNm3UQRSlT85CIjFqD7RH8ANgFYGanAV8GrgNagKvzW9roMK68hCPra9RhLCKj1mBBUOTu28L9NwJXu/vN7v7/gDn5LW30aGpI8/DaZnbu6o67FBGRfTZoEJhZ5neNXwH8NWtcPn/4flRpakzT1eMsVj+BiIxCgwXB9cDdZnYb0A7cA2Bmc4iahwSYN/MgilOm5iERGZUG/Fbv7l80s78AU4A73d3DqBTwwXwXN1pUlRVz9DSdTyAio9NgRw1VAovd/Tfu3mZmh5rZR4Aj3X3JyJQ4OjQ1pnlkbQttneonEJHRZbCmoT8Cs2B3c9ADQAPwfjP7Un5LG12aGuro6XUeWr1t8IlFRArIYEFwkLs/Fe6/Hbje3T8InA2cl9fKRpkTZh5ESZHOJxCR0WewIPCs+y8H/gzg7ruA3nwVNRpVlBZx7PRaFqjDWERGmcGC4BEz+3roF5gD3AlgZrX5Lmw0ampIs2xdC9s7uuIuRURkyAYLgncDW4j6CV7p7jvD8LnA1/NY16g0vzFNr6N+AhEZVQYLgmrgd+5+mbs/nDW8lagjWbIcP+MgSotTOp9AREaVwYLgO0BdH8PrgW8P9EQz+7GZbTKz5f2MP93MWsxsabh9emglF67ykiKOm16rDmMRGVUGC4Kj3P3u3IHu/ifg6EGeey1w1iDT3OPux4bb5weZdlRoakyzYn0rLe3qJxCR0WGwICjZz3G4+9+BxDWWNzWkcYcHVyXupYvIKDVYEDxlZufkDjSzs4GVw7D8JjN72MzuMLMjhmF+sTt2Ri1l6icQkVFksCuIfhj4g5m9AVgchs0DmjjwE8qWADPdfUcIm1uBQ/qa0MwuBS4FmDFjxgEuNr/Kios4YeZB6icQkVFjsD2Cc4F/Be4DZobb3cDR7v7kgSzY3VvdfUe4fztQYmZ9dUzj7le7+zx3nzdx4sQDWeyIaGpI8/jGVpp37oq7FBGRQQ0WBNOArwBfJdoT2AU8B1Qe6ILNbLKZWbh/UqhlTHyNbmqM+gkWrFQ/gYgUvsEuQ/0xADMrJQqCFwPvBH5oZs3uPre/55rZ9cDpQJ2ZrQU+Q+hgdvergNcB7zOzbqLfOrgo6zLXo9rR02qpKCliwcqtnHXk5MGfICISo6H+ylgFMB6oCbf1wLKBnuDuFw8y/krgyiEuf1QpLU4xb9ZB6jAWkVFhwCAws6uBI4DtwELgfuAb7q7fZBzE/IY0X/vTE2zd0Um6uizuckRE+jVYH8EMoAzYCKwD1gLNea5pTGhqTAOwUOcTiEiBGzAI3P0s4ET2XGDucuAhM7vTzD6X7+JGs6Pqa6gqLVLzkIgUvEH7CEIH7nIzayb6wfoWonMITiLqAJY+lBSlmDdrgs4nEJGCN9hvFn/IzG4wszXA34kC4AngtcCEEahvVGtqTPP0ph1s3t4ZdykiIv0abI9gFnAT8BF335D/csaWpoaon2DByq28+pipMVcjItK3wfoIPuruNykE9s8RU8dTXVas5iERKWiDHTUkB6C4KMVJsyfod4xFpKApCPKsqSHNyi1tPNfaEXcpIiJ9UhDkWeZ8ggVqHhKRAqUgyLPDp4xnfHmxzicQkYKlIMizopRx0uy09ghEpGApCEZAU2Oa1Vt3sqGlPe5SREReQEEwAjLnE6h5SEQKkYJgBBw2eRy1lSUKAhEpSAqCEZBKGSfPnsCCVQoCESk8CoIR0tSQZs22dtY+vzPuUkRE9qIgGCFNjXWA+glEpPAoCEbIIZOqmVBVqusOiUjBURCMkFTKmN8wgYUrtxH9xIOISGFQEIygpoY065rbWbNN5xOISOFQEIygzHWHHli5JeZKRET2UBCMoMaJ1dRVl6nDWEQKioJgBJlF/QQL1E8gIgVEQTDCmhrTbGztYPVWnU8gIoVBQTDCdN0hESk0CoIRNruuiknjynQ+gYgUDAXBCDMzmhqj3ydQP4GIFAIFQQyaGtJs3t7JPze3xV2KiIiCIA57zidQ85CIxC9vQWBmPzazTWa2vJ/xZmZXmNnTZvaImR2fr1oKzYwJlUypKWeBOoxFpADkc4/gWuCsAcafDRwSbpcC389jLQXFzGhqUD+BiBSGvAWBu/8d2DbAJBcA13lkAVBrZlPyVU+hmd+YZmvbLp7atCPuUkQk4eLsI6gH1mQ9XhuGvYCZXWpmi8xs0ebNm0ekuHzT+QQiUijiDALrY1if7STufrW7z3P3eRMnTsxzWSNj+oRK6msrFAQiErs4g2AtMD3r8TRgfUy1xKKpMc3CVVvp7VU/gYjEJ84g+C3wtnD00Hygxd03xFjPiGtqSPP8zi6eeG573KWISIIV52vGZnY9cDpQZ2Zrgc8AJQDufhVwO3AO8DSwE3hHvmopVPMb9/QTHD5lfMzViEhS5S0I3P3iQcY78P58LX80qK+tYMaESh5YuZV3vmR23OWISELpzOKYNTWkeXDVNvUTiEhsFAQxe/GcNC3tXXzr/56kR2EgIjFQEMTs7COncOFx9Vzx16d5y48W8lxrR9wliUjCKAhiVlqc4htvOIavve5olq5p5pxv38NdT2yKuywRSRAFQQEwM14/bzq/++ApTBxXxiU/eYgv3f4YXT29cZcmIgmgICggcyaN49b3n8KbT57BD/6+ktdf9QBrtum3jUUkvxQEBaa8pIgvXngU333T8fxz0w7OueIe7liWqPPsRGSEKQgK1LlHT+H2y06lYWI17/vFEj516zI6unriLktExiAFQQGbPqGSG9/TxKWnNfDzBc/ymu/ex9O6bLWIDDMFQYErLU7xyXMO5yfvOJFN2zt59Xfu5abFa+MuS0TGEAXBKPGyQydxx2Wncsz0Gj5248N89FdL2dHZHXdZIjIGKAhGkYPHl/OLd83nI2e8iFuXruPV37mXFetb4i5LREY5BcEoU5QyLjvjEH757vns3NXNhd+9n5/ev1q/fSwi+01BMErNb0hz+4dO5ZQ5aT7z2xW89+eLadnZFXdZIjIKKQhGsXR1Gde8/UQ+de7h/PXxTZxzxT0sfub5uMsSkVFGQTDKpVLGu05t4Kb3vphUCt7wgwf43l1P67LWIjJkCoIx4pjptfzhQ6dy1pGT+eofn+DtP3mQzds74y5LREYBBcEYMr68hCsvPo7/ufAoHly1jXOuuIf7nt4Sd1kiUuAUBGOMmfGmk2dw2wdOoaaihLdcs5Cv/+kJunUlUxHph4JgjDps8nh++4FTeP0J07jyb09z0dULWN/cHndZIlKAFARjWGVpMV993TF8+6JjeWxDK2d/+x7+/OhzcZclIgVGQZAAFxxbz+8/dCrTJ1Tw7usW8bnfraCzW1cyFZGIgiAhZtdVcfP7XswlL57FT+5bzb98/35Wb2mLuywRKQAKggQpKy7is+cfwQ/fNo8129o57zv3ctvSdXGXJSIxUxAk0JlzD+aOy07lsMnjuOyGpXz8pofZuUtXMhVJKgVBQk2treCGS+fzgZfN4cbFazn/yvt4YuP2uMsSkRgoCBKsuCjFx151KD9758k07+zi/Cvv5ZcLn9WVTEUSRkEgvOSQOu647FROmj2BT/5mGR+4/h+0duhKpiJJoSAQACaOK+On7ziJj591KH9cvpHzrriXh9c0x12WiIyA4nzO3MzOAr4NFAE/cvcv54w/HbgNWBUG3eLun89nTdK/VMr4t9PncPLsCXzo+qVc+L37OHTyeI6qH89R9TUcWV/D4VPGU15SFHepIjKMLF/twWZWBDwJnAmsBR4CLnb3R7OmOR34mLufN9T5zps3zxctWjS8xcoLNO/cxY/vW83SNc0sX9fCtrZdQPQLaYdMquao+hqOmhaFw1yFg0jBM7PF7j6vr3H53CM4CXja3VeGIm4ALgAeHfBZUhBqK0v56JkvAsDdWd/SwbK1LSxf18KydS389fFN3Lh4LbAnHI6sr9m95zB3yngqShUOIqNBPoOgHliT9XgtcHIf0zWZ2cPAeqK9gxW5E5jZpcClADNmzMhDqTIQM6O+toL62grOOnIyEIXDhpYOlq3bEw53PbGJm0I4pAwOmTQuhMN4jppWw9wpNQoHkQKUzyCwPobltkMtAWa6+w4zOwe4FTjkBU9yvxq4GqKmoWGuU/aDmTG1toKptRW86og94bCxde89h7uf3MzNS/aEw5ysPYej6muYO3U8laV57aoSkUHk8xO4Fpie9Xga0bf+3dy9Nev+7Wb2PTOrc3f9msooZGZMqalgSk0Fr8wKh+daO1kWgmH5uhbueWoLtyyJLm2RMmicWL27SSnacxhPVZnCQWSk5PPT9hBwiJnNBtYBFwFvyp7AzCYDz7m7m9lJRIezbs1jTTLCzIzJNeVMrinnzLkH7x7+XNhzyITDvU9v4ZZ/rAvPyQmH+hqOmKpwEMmXvH2y3L3bzD4A/Ino8NEfu/sKM3tvGH8V8DrgfWbWDbQDF7lOa02Eg8eXc/Dccs7ICodNrR177Tnc/88t/CYrHBrqqnaHQ+PEambXVTHtoAqKi3Q6jMiByNvho/miw0eTZdP2jqi/YW3r7oDY2Nqxe3xxypg+oZJZ6Upm1VUxu66KWeno79TaCopSfXVViSRPXIePihywSePKeflh5bz8sD17Dlt3dLJqSxurtrSxemsbq7fsZNWWNhau2sbOXXt+cKe0KMWMdGUIhhAU6Spm1VUxeXw5KYWECKAgkFEoXV1GurqMebMm7DXc3dm0PQqJ1VvaWLU1+rt6y07ueWoznd29u6ctL0kxc0LYg6gLQRH2JCaOK8NMISHJoSCQMcPMor6H8eXMb0jvNa63Nzq0dfeeRNibeGrTdv7y+HN09expIq0qLWJmOhMSlcyuq94dFBOqShUSMuYoCCQRUqk95z2cMqdur3E9vc765vbdIZFpclqxvoU/rthIT++ekBhXXry7HyKzJzG7rprZ6SpqKktG+mWJDAsFgSReUehwnj6hktNeNHGvcV09vax9vj1qagoBsWpLG0uefZ7fPbKe7GMtaitLqK0oobq8mKrSYsaVF1NVFt3Ghb/VmVt55nER1WUlVJUVMS781VFQMtIUBCIDKClKMTscjfSynHGd3T2s2baTVVt2snpLG89sa6O1vZsdndFtfXMHbbu62dERPc7uoxhIWXFqd4hU5wRJVVkImNIoTKrLil4YMFkhVKJQkSFQEIjsp7LiIuZMGsecSeOGNH1XTy9tnXuCoq2zm+0d3bR19kT3w7DM+B0dex5vbO3Ya1xH19BCpbQ4xbiyYipKi6gsLaKitJjKkuh+eWnR7vsVpcVU7p4m/C3ZM7wiTFdZumdeCpmxQ0EgMkJKilLUVpZSW1l6wPPq7umlrbOHHVl7HJlwyTzOBMf2zm7ad/Wwc1c37V29tO/qZmNrVxiWGd6zV4f50F6PUZ4dECV7B0kmNPYaXrJ3mFSUFFFWUkR5SYqKkiLKS4p2/y0rTukQ3xGiIBAZhYqLUtRUpoa1g7qrp5f2rp69A2L3/R7au7qjv+G2c/e0e4bv3NXD9o5uNrV2srNrz/Pbu3rYn3NXy4pTWeEQ3S/vIzj6DpMUZVnBMtA8ykuKEn3yoYJARIBoj6WkKMX48uE/+snd6ejq3b33kR0QHbtvvXR0ZYZFodQZxmUP6+jqobOrly07du01rjPc7+7dv6sllBalKAvhUFqcim5FUZiUFaX2Grb7fmaacL9sr2mK+pxmz3z3nldZ0Z7pRzqUFAQikndmFjUTjcDvUXT19O4VLB05IZIdJu05QZOZvrO7l13dvdHfnl52dUd7Ps3t0fDdt55eOrt66eyJHg+XopTtHRLh75tOmsG7Tm0YtuVkKAhEZEzJ7NmMKx/Z5bo7XT0egiM7LPYEy+5w2R0wYVgfz9lr2jBNXXVZXmpXEIiIDAMzo7TYKC1OQX6213mj479ERBJOQSAiknAKAhGRhFMQiIgknIJARCThFAQiIgmnIBARSTgFgYhIwpnvz5WgYmRmm4Fn9vPpdcCWYSxnuBRqXVC4tamufaO69s1YrGumu0/sa8SoC4IDYWaL3H1e3HXkKtS6oHBrU137RnXtm6TVpaYhEZGEUxCIiCRc0oLg6rgL6Eeh1gWFW5vq2jeqa98kqq5E9RGIiMgLJW2PQEREcigIREQSLhFBYGY/NrNNZrY87lqymdl0M/ubmT1mZivM7LK4awIws3Ize9DMHg51fS7umrKZWZGZ/cPMfh93LRlmttrMlpnZUjNbFHc9GWZWa2Y3mdnjYT1rKoCaDg3vU+bWamYfjrsuADP7SFjnl5vZ9WY2wr9z1jczuyzUtCIf71Ui+gjM7DRgB3Cdux8Zdz0ZZjYFmOLuS8xsHLAYeI27PxpzXQZUufsOMysB7gUuc/cFcdaVYWYfBeYB4939vLjrgSgIgHnuXlAnIZnZT4F73P1HZlYKVLp7c8xl7WZmRcA64GR3398TRYerlnqidX2uu7eb2a+B29392pjrOhK4ATgJ2AX8EXifuz81XMtIxB6Bu/8d2BZ3HbncfYO7Lwn3twOPAfXxVgUe2REeloRbQXxjMLNpwLnAj+KupdCZ2XjgNOAaAHffVUghELwC+GfcIZClGKgws2KgElgfcz0AhwML3H2nu3cDdwMXDucCEhEEo4GZzQKOAxbGXAqwu/llKbAJ+LO7F0RdwLeAjwO9MdeRy4E7zWyxmV0adzFBA7AZ+EloSvuRmVXFXVSOi4Dr4y4CwN3XAV8HngU2AC3ufme8VQGwHDjNzNJmVgmcA0wfzgUoCAqAmVUDNwMfdvfWuOsBcPcedz8WmAacFHZPY2Vm5wGb3H1x3LX04RR3Px44G3h/aI6MWzFwPPB9dz8OaAP+I96S9ghNVecDN8ZdC4CZHQRcAMwGpgJVZvaWeKsCd38M+ArwZ6JmoYeB7uFchoIgZqEN/mbgF+5+S9z15ApNCXcBZ8VbCQCnAOeH9vgbgJeb2c/jLSni7uvD303Ab4jac+O2FlibtTd3E1EwFIqzgSXu/lzchQRnAKvcfbO7dwG3AC+OuSYA3P0adz/e3U8jauYetv4BUBDEKnTKXgM85u7fiLueDDObaGa14X4F0Qfk8ViLAtz9P919mrvPImpS+Ku7x/6NzcyqQmc/oenllUS787Fy943AGjM7NAx6BRDrgQg5LqZAmoWCZ4H5ZlYZPpuvIOq3i52ZTQp/ZwCvZZjft+LhnFmhMrPrgdOBOjNbC3zG3a+Jtyog+ob7VmBZaI8H+KS73x5fSQBMAX4ajuhIAb9294I5VLMAHQz8Jtp2UAz80t3/GG9Ju30Q+EVohlkJvCPmegAIbd1nAu+Ju5YMd19oZjcBS4iaXv5B4Vxq4mYzSwNdwPvd/fnhnHkiDh8VEZH+qWlIRCThFAQiIgmnIBARSTgFgYhIwikIREQSTkEgkiOcR7EwXJbh1CFMf/r+XgnVzD4cDqUUiY2CQOSFXgE87u7Hufs9eV7Wh4kubjZk4fwOkWGjIJAxx8xmhWvv/zBcv/3OcIZ07nQzzewvZvZI+DvDzI4FvgqcE66VX5HznBPN7P7wWw0PZs4ozhr/WTP7WNbj5aGeKjP7Q3jecjN7o5l9iOiaNn8zs7+F6V9pZg+Y2RIzuzFchyrzewefNrN7gdeb2YfM7NFQ+w3D/R5KsiTizGJJpEOAi9393eG68v8C5F6X6Eqi36j4qZm9E7jC3V9jZp8m+m2BD2RPHM7O/RXwRnd/KFzmuX2I9ZwFrHf3c8O8aty9Jfy2wsvcfYuZ1QGfAs5w9zYz+wTwUeDzYR4d7v6S8Pz1wGx378xcDkRkf2mPQMaqVe6+NNxfDMzqY5om4Jfh/s+Alwwyz0OBDe7+EIC7t4brww/FMuAMM/uKmZ3q7i19TDMfmAvcFy458nZgZtb4X2Xdf4To0hFvYZivRCnJoyCQsaoz634PQ9v7Hex6KzaEabrZ+3NVDuDuTwInEAXCl8JeR1/z/7O7Hxtuc939X7PGt2XdPxf4bpjn4vBDKiL7RUEgSXY/0VVMAd5M9DOFA3kcmGpmJwKY2bg+NsCrCZd6NrPjia5tj5lNBXa6+8+Jfvwkczno7UCmn2EBcIqZzQnPqTSzF+UWYWYpYLq7/43oR3pqgeohvF6RPulbhCTZh4Afm9m/E/2S14BX5nT3XWb2RuA7oRO5negS3dluBt4WmnYeAp4Mw48CvmZmvURXkHxfGH41cIeZbXD3l5nZJcD1ZlYWxn8qax4ZRcDPzayGaC/imwX4E5QyiujqoyIiCaemIRGRhFMQiIgknIJARCThFAQiIgmnIBARSTgFgYhIwikIREQS7v8DGWInDIDZTC0AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X = getEmbeddings('node2vec/simple_directed', G.nodes())\n", "\n", "def elbow(X,max_clusters=10):\n", "\n", " wcss = [] #for storing the intertia property\n", "\n", " for i in range(1, max_clusters):\n", " kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter=300, n_init=10, random_state=0)\n", " kmeans.fit(X)\n", " wcss.append(kmeans.inertia_)\n", "\n", " plt.plot(range(1, max_clusters), wcss)\n", " plt.title('Scree plot of WCSS for n clusters (elbow method)')\n", " plt.xlabel('n of clusters')\n", " plt.ylabel('WCSS')\n", " plt.show()\n", " \n", "elbow(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kmeans = KMeans(n_clusters = 3, init = 'k-means++', max_iter=300, n_init=10, random_state=0).fit(X)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [], "source": [ "def draw(graph, X, kmeans, labels=True, size=(15,15)):\n", "\n", " plt.figure(figsize = size)\n", " \n", " nx.draw_networkx(\n", " graph,\n", " pos=nx.spring_layout(graph),\n", " with_labels=labels,\n", " node_size=[n[1]*8 for n in G.degree()],\n", " node_color=kmeans.labels_,\n", " edge_color='grey'\n", " )\n", " \n", " plt.axis('off')\n", " plt.show()\n", " \n", "#draw(G, X, kmeans, labels=False)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def mds(graph, X, kmeans, n_components=2, label=True, size=(7,7), save=str()):\n", " embedding = MDS(n_components=2)\n", " X_transformed = embedding.fit_transform(X)\n", " X = pd.DataFrame(X_transformed, index=graph.nodes)\n", " \n", " plt.figure(figsize=size)\n", " plt.scatter(X.iloc[:,0],X.iloc[:,1], c=kmeans.labels_)\n", "\n", " if label:\n", " for n, row in X.iterrows():\n", " plt.text(row[0],row[1],n, size=18)\n", "\n", " plt.xticks(size=18)\n", " plt.yticks(size=18)\n", " if save:\n", " plt.savefig(fname=f'images/{save}.png', bbox_inches='tight', dpi=500)\n", " \n", " plt.show()\n", " \n", "#mds(G, X, kmeans, size=(15,15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Multiple directed graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "G = nx.MultiDiGraph()\n", "\n", "for n, row in data.iterrows():\n", " G.add_edge(row.Source, row.Target)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "node2vec = Node2Vec(G, dimensions=dim, walk_length=wl, num_walks=nw, p=p, q=q)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = node2vec.fit(window=10, min_count=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.wv.save_word2vec_format('node2vec/multiple_directed')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X = getEmbeddings('node2vec/multiple_directed', G.nodes())\n", " \n", "elbow(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kmeans = KMeans(n_clusters = 3, init = 'k-means++', max_iter=300, n_init=10, random_state=0).fit(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "draw(G, X, kmeans, labels=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mds(G, X, kmeans)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Multiple directed and valued ties" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "G = nx.MultiDiGraph()\n", "\n", "for n, row in data.iterrows():\n", " G.add_edge(row.Source, row.Target, value=row.Weight)\n", " \n", "nodes = list(G.nodes())" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1bad191be8a1448ab68788bc5dfccfce", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Computing transition probabilities: 0%| | 0/59 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X = getEmbeddings('node2vec/multiple_valued_directed', G.nodes())\n", " \n", "elbow(X)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "kmeans = KMeans(n_clusters = 3, init = 'k-means++', max_iter=300, n_init=10, random_state=0).fit(X)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw(G, X, kmeans, labels=False, size=(7,7))" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Christian\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n", " return f(*args, **kwargs)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mds(G, X, kmeans, size=(15,15), save='7.11')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "list(G.nodes())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cluster_dict = {}\n", "\n", "for n in range(len(G.nodes())):\n", " cluster_dict[list(G.nodes())[n]] = kmeans.labels_[n]\n", "\n", "cluster_df = pd.DataFrame([cluster_dict]).T\n", "cluster_df.columns = ['cluster']\n", "\n", "cluster_df.to_csv('node2vec/multiple_directed_valued_clusters.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Contrasting the sojourner and the Israelites\n", "\n", "The sojourner is a curious participant in the network. On the hand, Leviticus 17-26 gives the impression that the sojourner is generally a person on the margins of society. However, in the network, he is situated safely at the core of the network. The network may be misleading because the native Israelites are distinguished as the collective group (\"Israelites\"), an individual Israelite addressee (\"2ms\") and an individual, third-person Israelite (\"an_Israelite\"). Of course, many other participants (e.g. the mother and the father) also belong to the Israelites as an ethnic category. In the text, however, they are not so much distinguished in terms of ethnicitiy as in terms of social role. Therefore, these other roles will be retained. The first three, however, will be merged into a single role in order to explore the role of the sojourner in contrast to Israelites:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "G1 = nx.contracted_nodes(G, \"Israelites\", \"2ms\")\n", "G1 = nx.contracted_nodes(G1, \"Israelites\", \"an_Israelite\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "node2vec = Node2Vec(G1, dimensions=dim, walk_length=wl, num_walks=nw, p=p, q=q, weight_key='value')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = node2vec.fit(window=6, min_count=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.wv.save_word2vec_format('multiple_valued_directed_Israelites')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'G1' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Users\\CHRIST~1\\AppData\\Local\\Temp/ipykernel_15980/1593341263.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetEmbeddings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'multiple_valued_directed_Israelites'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mG1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0melbow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmax_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mwcss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#for storing the intertia property\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'G1' is not defined" ] } ], "source": [ "X = getEmbeddings('multiple_valued_directed_Israelites', G1.nodes())\n", "\n", "def elbow(X,max_clusters=10):\n", "\n", " wcss = [] #for storing the intertia property\n", "\n", " for i in range(1, max_clusters):\n", " kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter=300, n_init=10, random_state=0)\n", " kmeans.fit(X)\n", " wcss.append(kmeans.inertia_)\n", "\n", " plt.plot(range(1, max_clusters), wcss)\n", " plt.title('Scree plot of WCSS for n clusters (elbow method)')\n", " plt.xlabel('n of clusters')\n", " plt.ylabel('WCSS')\n", " plt.show()\n", " \n", "elbow(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kmeans = KMeans(n_clusters = 3, init = 'k-means++', max_iter=300, n_init=10, random_state=0).fit(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "draw(G1, X, kmeans, labels=False, size=(15,15))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mds(G1, X, kmeans, size=(15,15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Case study: Narrative de" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Splitting the dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tf.app import use\n", "A = use('etcbc/bhsa', version='c', hoist=globals(), silent=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "phase1 = ((17,1),(24,23))\n", "phase2 = ((25,1),(26,46))\n", "\n", "def getClauses(phase, df=data, book='Leviticus'):\n", " \n", " first_clause = L.d(T.nodeFromSection((book, phase[0][0], phase[0][1])), 'clause')[0]\n", " last_clause = L.d(T.nodeFromSection((book, phase[1][0], phase[1][1])), 'clause')[-1]\n", " \n", " return range(first_clause, last_clause+1)\n", "\n", "phase1_cl = getClauses(phase1)\n", "phase2_cl = getClauses(phase2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1 = data[data.Clause.isin(list(phase1_cl))]\n", "df2 = data[data.Clause.isin(list(phase2_cl))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are all clauses included?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(data)-(len(df1)+len(df2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Node2Vec applied to each phase" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def Graph(df):\n", "\n", " G = nx.MultiDiGraph()\n", "\n", " for n, row in df.iterrows():\n", " G.add_edge(row.Source_label, row.Target_label, value=row.Weight)\n", "\n", " return G\n", "\n", "graph1 = Graph(df1)\n", "graph2 = Graph(df2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Model phase 1\n", "node2vec = Node2Vec(graph1, dimensions=dim, walk_length=wl, num_walks=nw, p=p, q=q, weight_key='value')\n", "model = node2vec.fit(window=6, min_count=1)\n", "model.wv.save_word2vec_format('multiple_valued_directed_phase1')\n", "\n", "#Model phase2\n", "node2vec = Node2Vec(graph2, dimensions=dim, walk_length=wl, num_walks=nw, p=p, q=q, weight_key='value')\n", "model = node2vec.fit(window=6, min_count=1)\n", "model.wv.save_word2vec_format('multiple_valued_directed_phase2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Phase 1:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embeddings1 = getEmbeddings('multiple_valued_directed_phase1', graph1.nodes())\n", "elbow(embeddings1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kmeans_graph1 = KMeans(n_clusters = 3, init = 'k-means++', max_iter=300, n_init=10, random_state=0).fit(embeddings1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mds(graph1, embeddings1, kmeans_graph1, size=(15,15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Phase 2:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embeddings2 = getEmbeddings('multiple_valued_directed_phase2', graph2.nodes())\n", "elbow(embeddings2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kmeans_graph2 = KMeans(n_clusters = 2, init = 'k-means++', max_iter=300, n_init=10, random_state=0).fit(embeddings2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mds(graph2, embeddings2, kmeans_graph2, size=(15,15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Export clusters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kmeans_clusters = pd.DataFrame([nodes, list(kmeans.labels_)]).T\n", "kmeans_clusters.columns = ['node','cluster']\n", "kmeans_clusters.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "kmeans_clusters.to_csv('node2vec/multiple_directed_valued_clusters.csv')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }