{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read the network\n", "\n", "Let us read the network and store it into a numpy array. Each row corresponds to a different contact.\n", "\n", "Each edge (row) is characterised by 4 features(the 4 columns of the array).\n", "\n", "- The timing of the contact\n", "- The identities of the nodes involved in the contact\n", "- The weight associated to the contact " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0 4 287 1]\n", " [ 0 12 287 1]\n", " [ 0 14 287 1]\n", " [ 0 15 416 1]\n", " [ 0 15 705 2]\n", " [ 0 15 738 1]\n", " [ 0 39 287 1]\n", " [ 0 43 50 12]\n", " [ 0 43 200 2]\n", " [ 0 43 520 2]]\n" ] } ], "source": [ "#Temporal network from M. Salathé, et al., PNAS 2010 107 (51) 22020-22025\n", "#https://www.pnas.org/content/107/51/22020\n", "\n", "net = np.loadtxt('school_salathe.csv').astype(int) # read network\n", "print(net[:10,:]) # print first contacts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the number of active nodes" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_node_activity(network):\n", " '''\n", " This function computes the number of active nodes during each time step\n", " \n", " Output:\n", " 1) List of snapshot timings\n", " 2) List with number of active nodes\n", " \n", " '''\n", " res = []\n", " times = np.unique(network[:,0]) # get snapshot timing\n", " times = sorted(times) # put times in increasing order\n", " \n", " # now loop over times, get the corresponding snapshot \n", " # and compute the number of unique node ids involved\n", " for t in times:\n", " snapshot = network[network[:,0] == t]\n", " res.append( len( np.unique(snapshot[:,1:3]) ) )\n", " \n", " return times, res" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,u'# active nodes')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1Wsmthzy2qprZ5UNi2fNCROIzoiTNz/5uLhdMH82Xf/sa//b71/v0kFwljR6wcXsjKyt3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\nulMiklTF6cK9X2x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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# To obtain the number of active nodes, first split the network into snapshots (the .groupby() method),\n", "# Then count the number of unique node ids in that snapshot. \n", "\n", "times, active_nodes = get_node_activity(net)\n", "plt.plot(times, active_nodes)\n", "\n", "plt.xlabel('Time', fontsize = 16)\n", "plt.ylabel('# active nodes', fontsize = 16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot number of active links" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_link_activity(network):\n", " '''\n", " This function computes the number of active links during each time step\n", " \n", " Output:\n", " 1) List of snapshot timings\n", " 2) List with number of active links\n", " \n", " '''\n", " res = []\n", " times = np.unique(network[:,0]) # get snapshot timing\n", " times = sorted(times) # put times in increasing order\n", " \n", " # now loop over times, get the corresponding snapshot \n", " # and compute the number of contacts involved\n", " for t in times:\n", " snapshot = network[network[:,0] == t]\n", " res.append( len(snapshot[:,1:3] ) )\n", " \n", " return times, res" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,u'# active links')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZwpMSeDROQcDLHT0ZQ1Oo/QwMGBqvejRNWUa0sTZgcjQ5KHSmKvkiTVW9Isfw\n9/Nsezdwd57nNmDJFgwdPw5cNJY5uoV8Gu4ZqQAPadL0xvKHZuo92Lk3WkSn45a6IImU2s0q3f1/\nimYKT0rg0TiVZ4e6ef1iKyTdGU0ws4CsRX2m+tBbxwFYHk22p95UZ8TPclGoGKCoRZiG8pD58dec\nWnUG3tKkcWScawI5ZHxrvNd+JFqkRwNWvzO3G5p8pfSjoTkcZHZLHS8e6sqMdfUlWDKzMe9r6jzt\n0SQz+RmwcqjGozkVVxQDGE4lEk8RCvgIDqlsyjTu81AcOJeMs0M4FPDcgk0rbDmcofFOB+d8FzWj\nZdmswa1oOqPxvO1nYKBM3GvVh2D9DpuyPRqTo8mJMTQuxWpCeeoPv8mDbS5yyTg71HuwRfxQie1c\nOI01uzxw0umNJ3Ne1IyWpbOa2H0sQjSeJJ5ME4mn8i7WBAj4rWR6NOGt4wAGZJwdGmsDxJJpYh5s\np1NOjKFxKfkUDwc8Gu/8KHv682ud1NcEvFkMkEeLxsFbHk0q7xqX0bB0ZhOqsPXVnoyhLWRowCoI\n8NpxAAMyzg6mDU1ujKFxKdFYKmfvqewcjVeI5NCicfBuMUBxOZpOD3QHGKuM81CWzhooCHC6IxQq\nbwbrOPBarg6s32H2sW2kAnJjDI1LieRZFOjFfkq9OdQ1HcIeLG8uJnTmpQ7O0QKhzdEwu6WO5rog\nLx7qHrb9jENdyJ/piu0VEqk0/Yn0oNCZkQrIzYgMjYhMFZG3isiVIjLZHqsVEWOwSkw+ad3aoNVP\nyUurjyOxZN7OwI5HYzeEcD2ptBJLpoetOqsNWtpBXsjRjFWLZigiwlJbmyZjaIb1aPyeKwbItRDZ\nqUAzHs1gijIQYvFlrPYv92Gpac6zn74X+FxZZjeBsdZf5D6ZOd0BvEJvIUNTEyCt0J9IV3hWo6Mv\nUVgiIJuWuhCdHmiAmu+iZiwsndXE1o5ujkdiwPA5mrqQ33PlzUPbz0DWOjcPhbYrQbGeyGeAjwM3\nAOcyuLHlL7D6lBlKSG+BH7/XNGkKGxpvtR9xOjbUFXFi9kpjzWJCgSNl6cwmYsk0m/Z3AvnVNR28\nmKtzCnJyeTReuhCsBMVexvwFcIOqflFEhh6RO4CFpZ2WIRpP5V3X4CVNGkfGOW+Oxj5hR2MpaKjk\nzEZHXxESAQ7NdUH2n+wjlVb8LpYoLnXoDAYKAv648zgip/a5G4qXPZrsFjRNphggJ8V6NLOxRMly\nEQfqSzMdg0OkQAK9qdY7rcidq9T8VWde82iKD51dumImL3d089l7tpBOuzcHVepiAIDTpjUQ8vvY\nezxKc10Q3zCG1pMeTY4cTX2FxtPkAAAgAElEQVQogIgJnQ2lWENzkBy9xWxWAbtLMx0DQDKVJpZM\n5w+deShHU6ihJgzIIHhFKiBahLqmw5Xnz+MTF57GnRv284/3vuDagodCYdrREvT7WDzDclGHKwQA\n6/v0WnlztuiZg88nNNaYNjRDKfbo+l/gn0TkOQY8GxWRxcCngVvKMbmJykA33UI5Gm8cyL39hfXo\nG2q81X4kEzor8sT8qTctJp5S/uu3Own6fVz/tqWu6ubsXNSUo0Hr0plNvHCwm+ZhSpvBXrAZT3mi\n27VDLo8GvHUhWCmKPbq+AJwPPA44zTb/F2gH/ojVnt9QIgb0QQpXnXnhR5lPxtnBOcF5pd+Z43kV\nmzwXEf5h3ekkUmm+//vdhAI+PnPJEtf836KJ0kkEDMXp5FyMR1NfEyCVVuKpdM7mq27E8WiGHtuN\ntUHPXAhWiqIMjar2icgbgPcDb8YqADgO/AvwI1U132oJieZR13RwNGn6E8Ov56g2vcOEzjJyzh4J\nm4wkR+MgInz+LWeQSKW55fFdBP3C3158uiuMTalknHOxdFYzMHxpM0BdcKCDs3cMjdWPcKikt9eq\nQitB0UeXqqaA/7FvhjLihJEa8ladDdTqu93QRIbzaGocvXhvXKtERxg6cxARvvC2ZSRSyk2P7STk\n9/PJNy4qxxRHRCllnIdyhi0NUJxH4xSFpGgJl3wqZaG7LzFoDY1DY63RpBlKsQs27xGRy0Vk+CNm\n+H3dKiJHROSFrLHJIvKwiGy3/06yx0VEvikiO0Rks4iclfWaK+3tt4vIlVnja0Rki/2ab4obLhtH\nSGQYISovtbnoGSZHk/FoPJKjGVhHM/ITs88n3Hj5ct61po2v/foVntx1vNTTGzGllHEeSmNtkOvf\ntpR3r20fdltnXVKfRy444NSGmg5NdQFP/DYrSbFVZ0uAe4AOEblJRM4bw3veBqwbMnYd8IiqLgIe\nsR8DXAIssm9XAzeDZZiwZKDPBc4BrneMk73N1VmvG/permc4IapMB2cPuOcZjyZPeXNt0IdPvOPR\n9I0idJaNzyfccNkyQgEfD714uJRTGxWOgS+VFs1QPvKa+Syf3Tzsdpkyd49ccAD0xAbLODs01QYz\n+RuDRVGGRlWXAmcDPwTeCfzB9iT+UUQWjOQNVfVx4MSQ4cuA2+37twOXZ43foRZPAi0iMhMrT/Sw\nqp5Q1ZPAw8A6+7kmVX1CrVrSO7L25Rmiw4QzMpo0Hli0WUjGGayQUn0okNnO7UQTKYJ+GZN2SzgU\n4E8WTOGxbUdKOLPRkTnWqiwL7niIXipxzuvR1AboiSVdvXaq0hT9a1HVZ1X1WqANeBvwDPAPwHYR\n+d0Y5zFdVTvs9+kAptnjs4H9WdsdsMcKjR/IMX4KInK1iGwQkQ1Hjx4d4/RLy3AJdC95NIVknB28\n1MHZ0qIZ+9X/hUumsftYhF1He0swq9EzUEpf3VxfpkOERzxbKJyjUfXOIuRKMOLLMlVNqep6VX0/\nlndzCKv0uRzkyq/oKMZPHVS9RVXXqura1tbWMUyx9AxUAuXzaLyTo7E6HBQ+iXlpVXi0RNotFy6x\nrqUe3Vpdr2a4BbWVot6jHk1TnhwNYEqcsxixoRGRhSJyvYi8AjyAdXL/yhjncdgOe2H/dX59B7DW\n6ji0YRm2QuNtOcY9RSReuBggo0njgQO5N5bMm59xCNd4Z1V4qRpQtk8Os2haQ9XDZ8MVnlQKJ3Tm\npX5nVugst0djPe/+C8FKUWzV2SQR+aiI/AF4BfhbrA4B64B2Vf37Mc7jPsCpHLsSS3rAGf+QXX12\nHtBlh9YeBC625zUJuBh40H6uR0TOs6vNPpS1L88QiSWpC/rzNmLMaNJ4wKMppr1JfSjgmQWbffFU\nyUrKL1wyjad3n6hqfmo064LKwcB6Km8cB/2JFPFUOk+Oxnty6+WmWI/mVeDbQAT4MFZO5UOq+rCO\nsIGTiPwEeAI4XUQOiMhVWJ0F3iQi24E3MdBpYD2wC2uB6HeBvwZQ1RNYi0WfsW832GMAHwO+Z79m\nJ5bX5SkiBbRoHJrqvNFYszdPwjSb+hovhc5K11L/giXTSKSU32+vXo4wEk8SCvjGVNxQCrxWDNCd\n6XN26rE9IOfs/t9npSjWX/488EMnYT8WVPWKPE9dlGNbBa7Js59bsQTYho5vIH8DUE8QLdC52aGp\nNuCJK6ZIPMnk+sK9rsIh7xQDRBOpjEzzWFkzdxKNtQEe3XqEdctnlmSfIyUaS+Vd41RJagI+/D7x\nTDGAsz4sZ3mzh4p1KkWx5c1fLoWRMRRHbyw1bMzcSx7NcEbTS8UAffFkUVo0xRD0+3jd4lYe23a0\naqWwkViy6mEzsMrcw0Hv5OpyyTg7NHpo+UGlyHsGEJEPAb9U1eP2/YKo6h0lndkEJhpP5m2o6dBU\nG/SETHBvLEnjMIbGS+XNpVajvPD0afxycwcvHOpiZVtLyfZbLJF46SUCRouXjoNcMs4OJnR2KoWO\nsNuA87CaZ942zH6cxZGGEhCJp4btD9VUF2TfiWiFZjR6eosIAzbYORovdKMuZTEAwBtOb0XEKnOu\nhqEppORaacKhQKabtNvJJePsUBPwUxv0eaIqtFIUOgPMBzqy7hsqRCSWZHZLbcFtrByNu6+YHBnn\n4XIA4VCAtOKJbtSl9mimNNSwqq2Fx7Ye4do3Li7ZfoslUgbRs9FSF/QT9Uj1YS7Rs2waTRuaQeQ9\nwlR1b677hvITjQ2/KNDJ0bjZC3DyLsMZmoHOvUlXG5p0WulLpDINIEvFRUum8ZWHX+FoT4zWxpqS\n7ns4ovFUxd8zH/UeWk9VKEcD3inWqRTFrqNJicg5eZ5bIyLeODo8QqQIL6CpNkgiZWnSuJXhGmo6\nZNqPuDw+3590mp2W1hheYHcJ+E0VFm+WQ8Z5tNR5KXTWn0Akf484S/zMeDQOxRbPF7pk9pOnzYth\n5KhqUZVA2Zo0bqXY9iaZzr0urzwr1+LGZbOamN5UU5UuAW7K0dSHvBQ6S9JQE8CXZ1G1FXHwxmep\nBAUNjYj4RMQ5Cn324+xbPVYr/2Nln+kEIZ5Kk0xrEeto3N/vLBNeGDZ05o2Gio7HVerQmYhwwenT\nePyVY8STlfVQXZWjCXkndNbdn8ibnwErpGZyNAPkNTQicj2QAOJYHssf7MfZt27gn4D/LftMJwiO\nHsew5c0eWBSW+SxF5mh6XR46iyacvmCl9wAuWDKN3liSDXuGKmiUj2QqTSyZrnqfM4f6UMD1FxsO\n+SQCHJpqgyZHk0WhI+w39l/BMibfZ3ALfoAY8BJwf8lnNkHJNDksojMAuHtRWG/MMoLFVJ0Brg+b\nOFfb5ShY+NPTphLy+3h06xHOP21qyfefC7dIBDiEveTR9BX2aJqMRzOIQlVnvwV+CyAiCnxXVT3X\nCdlrOD+0YYsBPODROB7KsFVnmYaK7j7JZNQ1S9QZIJv6mgDnLpjMo9uO8Pm3Li35/nMxILDnDo8m\nHAoQS6ZJpTVvQ1m30NOfZFaBJQhNdUFiyTSxZKqgFtNEodhigO8ADbmeEJHFIlKZS7AJQG+suPCM\nF3I0vbYRHO6K2UlGuz1sMlAMUJ4T84VLprHraIS9xyNl2f9QIsPoHlWacMgbxwFYMs65ugI4DHQH\ncP9nqQQjMTSfzvPcp+znDSWg2KtML2jSOB7KcOXNjsfjdr14539TrrU+jhjaQy8eZoRN0UeFW2Sc\nHbykSVNMjgbcfSFYSYo9wv6UPF2UgYewJAQMJWCgGKDwv8YLmjQ9/UmCfhk2dFAT8OETXK9J01dm\n7Za5U+pZ2FrPjetf5j8e3MqkcIjJ9SGmNISYXF9D26Q6rn3jopKFYoot1qgUAwt33W1oVHVYQ2M8\nmsEUe4RNArryPNcNTCnNdAwDa0+GP5m4vYNzJJYsqgW9iHiig3MlRMK+/f6z+MOOYxyPxDnRG+d4\nJM7xSIw9x07yi+cPsXbuJC46Y3pJ3mskx1olqAt6pMw9niKV1sLFAB7IoVaSYg3NAeBc4JEcz53L\nQE+0USEipwN3Zg0twKp0awH+EnCUoT6rquvt13wGuApIAZ9Q1Qft8XXAN7AWkn5PVb+Eh4gOI+Oc\njdvbXBTTUNPBC517+xLlqzpzOGNmE2fMbDplPBpPsvz6B3l+f2fpDM0IjrVKUJ/J1bn7OBhoP2Ny\nNMVS7BH2M+CzIvK8qv7SGRSRtwDXATePZRKqug04096nHzgI/Bz4CPA1Vf3P7O1FZCnwPmAZMAv4\ntYg4HQlvwlLpPAA8IyL3qepLY5lfJYkUWXUG7vdoeov0aMAbKpvReBK/TwhVQY0yHAqweHojmw7k\nCyyMnKgLy5vB/YamOyMRYHI0xVKsobkBeB1wn4i8imUIZgMzgCeBfy7hnC4Cdqrq3gLNIi8Dfqqq\nMWC3iOwAnF5sO1R1F4CI/NTe1juGJpZEBGqDw5/M3K5J09s/AkMTCrj+BBONpwgH/VVrYrp6Tgvr\nt7xaskaqmTVbLvFovLKeqqcYQ2NCZ4MoVmEzCrweK4z1ONCJtcbmKuD19vOl4n3AT7Ief1xENovI\nrSIyyR6bDezP2uaAPZZv3DNEYinqQ4GiTiRu76cUiSeHrThzCIf8mdJut1JqLZqRsqqtha6+BHuO\nl+bnVmwXikrhHY8mv4yzQ33Ij09M6Myh6BiAqiZU9VZVvUJVL1bV96vqbapasm9SRELA2xloaXMz\nsBArrNYBfMXZNNcUC4zneq+rRWSDiGw4evRork2qQjSeLDqU4XZNmpHkaOpr3N9+pNRaNCNlVbsl\njPb8/s6S7C8aT1IT8BGoQigwFxmPxuXHgWM8mgpcRImI1cHZxb/PSuKOI2yAS4DnVPUwgKoeVtWU\nqqaB7zIQHjsAtGe9rg04VGD8FFT1FlVdq6prW1tbS/wxRs9I2rZna9K4kd7+4WWcHcIh9xcDROOl\n16IZCYunNxIO+dlUIkMTiRd/IVAJPOPR9OWXcc7GaqzpbqNZKYo+ykTkzcBHgdOBob0XVFUXlmA+\nV5AVNhORmarqVLS9A3jBvn8f8GMR+SpWMcAi4Gksj2aRiMzHyiO9D3h/CeZVMUbStj1bk8aNgmGR\nEXg0DR4oBuhLJKsaZvL7hOWzm0tmaKKx6npoQ6kLesPQDCd65tBkNGkyFCt8dimwHggDS4CtwD4s\n7yGNlbcZEyISxqoWuydr+D9EZIuIbAYuwOpCgKq+CNyFleT/FXCN7fkkgY8DDwIvA3fZ23qGkbRt\nd7MmTTqtRQm4OYRDAY94NNU9Ma9ub+GlQ90lkROIxN0jEQDg84kl5+zyC46e/gQBe66FaKwNuDqH\nWkmKDZ39I1bZ8KX248+r6huwyov9wANjnYiqRlV1iqp2ZY19UFVXqOpKVX17lneDqt6oqgtV9XRV\nfSBrfL2qLrafu3Gs86o0IwlnTKm3JHjv3XSwnFMaFcXKODvU1/iJxJOuDQOCVQxQbQ9gVXsL8VSa\nlzu6x7yvSCzlmtJmBy90cHa6AgxXsNNUZ3I0DsUamiXAL7C8F8UOuanqK8AXsAyRoQSMJJxx4ZJp\nXLx0Ov+2fitfeWibq07SvUWqazqEQwHSiqulqa1igOp6AJmCgANjD5+5LUcD9sJdlxua7v7CDTUd\nTI5mgGINTRpIqnUmOwrMyXruEFZlmKEEjGSRYyjg4zsfOIv3rm3nW4/u4HP/7wVSaXcYG2eNRrHl\nzQN9rtz7w4zGk1UPnc1qrqW1sYZN+8ZuaNyWowEIB91ffdjTn8yErQthcjQDFHs5sw2YZ9/fAFwr\nIn8AklhdnfeUfGYTlJFeNQf8Pr70f1YwuSHEzb/ZSWc0ztfee2bVNTCcK7mGIkMz9ZnFeqk8ghTV\nx1mwWU1EhFVtLWwqlUfjohwNeMOj6elP0FgzvEfTVBugN5YknVZ8LtfXKTfFejQ/As6w71+PlZs5\nALwKXIjVl8wwRlTVDmeM7GQmIvzDuiV8/i1nsH7Lq3zkB89UffFjJCN6NvwPEtzv0agqfQl3eACr\n57Sw62iErjHG/6PxlPtCZx7K0QxHU10QVeh16TFdSYrtDHCTqv69ff9ZYAXwV1hVYGeq6s/KN8WJ\nQ18ihero27b/xWsX8JV3r+Kp3Se44pYnOd4bK/EMi8eRcS7WaDpenFulAvoTaVSp6joah1VtVp5m\n8xi9mt5YsuhS+koRDgVceww4dPcVn6OBynUHcLOOz6gWbKrqAVX9nqp+00sNK91OKVqC/J81bdzy\nwTW8criHP799Q0nKYEeDI+NcTIgB3K9FMtBVu/on5pXtzcDYOgQkUmniybT7Qmchf6ZLtlsZSY4G\nKtNY8+ndJ1j5zw+y40hv2d9rNLitM8CEZiQSAYW46IzpfP29Z/L8/k7+86FtpZjaiClWxtnB7Q0V\nnXBOtYsBwDqBLWytH9PCzUpo64wGt4fO0mmlN54s0qOxtqmER/Po1iMkUsoTu46X/b1GgzE0LmKk\nJcGFuGTFTD543lxueXwXj249POb9jYS+eIr1L7xKKOAr6gcJWXLOLj3JOFfZbjkxr2pvYdP+rlGX\ntBcrGV5prIW77rzYAOiJJVEt3OfMIbOguiIejWVgNu47Wfb3Gg3G0LiIUuuDfO4tZ3DGzCY+fdfz\ndHT1lWSfw9GfSPGXd2zgmT0n+NI7VxAKFHeIDfS5cudJxm0ewOr2Fo71xjjU1T+q10dKeFFTSsIh\nP9FEylVrwrIpRiLAwQmd9cTKa2j64ik22zpFpSh7LwfG0LiIUv/4a4N+vv3+1cSSaT75k00kU+XN\n1zhG5g87j/Hld63inWe1Ff1a5zNXu1ouH44BdOSGq42zcHO0Jxa3SQQ4hEMB1MULdwc6NxdfDFBu\nFdzn9p0kmVbOnT+ZXccinIy4T6PKGBoXMfDjL93JbGFrAze+YzlP7znBNx/ZXrL9DiWWTPHRHz7L\n77Yf49/fuZJ3rSneyADUBHz4BNf2O+tzmUezZEYToYBv1B0C3Cbj7OB2z7YYGWeHxgoVAzy1+wQ+\ngav+dD5ASdZYlZqiDY2IzBERdx2V44xImSqb3rG6jXetaeNbj+3gjzuOjei1qsqRnn52HOnN23Ug\nlkzxsR8+x2+2HeWL71zBe85uz7ldIUSE+pB7Ozi7LXQWCvhYNqtp1B6NY9Dd2OsM3NvBeUAiYPhT\nYSjgozboo6fMXvrTu4+zbFYz5582FZ/ARheGz0ZiOHYDq4HNACLyOuBZVY2UY2ITEScJWmwLmpFw\nw2XL2LjvJJ+8cxPrP/FaWhtrMs+l08qRnhgHO6PsORZlz/EIu49Zt73Ho5lwVmNNgLPmTuLseZNY\nO28yZ7a34BPhmh9t5NGtR/jXy5dzxTlz8k1hWOpr3NvBuc9FVWcOq9pauPOZ/SRT6RGLl7nXo3HE\nz9x5HDj5lmIMDdhtaMro0cSSKTbu6+SD582loSbA4umNriwIyPttichfAc8Cm1U1TpZ6pYj4gceA\ns4Hnyj3JiYJTcVWORXThUICbPnAWl337D/zFHRtYPK2BAyf7ONjZR0dXH4nUgLfiE2ibFGbe1HrW\nzp3E/Kn1hGsCbNrfyYY9J/jPhyxF0qBfmNZYy8HOPm64bBl/dt7csc3R7uDsRkpVel5KVs9p4bY/\n7mH7kV7OmNk0otcOdG5wz+eBgWPf7aGzQjLO2ZS7seaWA13EkmnOmT8ZgNVzJvHLzYdc1/am0FH2\nSWAxkBKRl7C6Nr9BRI4CR8gtm2wYA5FYkoBPCJVJWnfJjCb+9fLl/NO9L/JqVx+zW+pY1d7CpStm\nMntSHW0tdcyZEqZ9Ujhntdh71lohsc5onOf2neSZPSd54WAXn7joNN579ug9GYd6F68Kj7qsvBkG\nOgRs2t85YkOTMZxuC525XPysWNEzB0cFt1w8tfsEAGfPcwxNCz95eh+7jkU4bZp7mgbm/bZUdamI\n1ANrgLXAfwL/AnwNS71SgYtF5ICqHinFZERkD9ADpLC6Ra8VkcnAnVhNPfcA71HVk2KJQXwDSyMn\nCnxYVZ+z93Ml8Hl7t/+qqreXYn7lxuk9NZzOxVh499p23rWmbUzv0RIOceGS6Vy4ZHoJZ2adxF27\njiaewidW0YJbmDslTEs4yPP7O0ccsnQ8mmo3CR2K20Nn3X0JQgFf0U1rG2uDY+5JV4indp/g9OmN\nTKoPAXDWHOviY+O+k64yNAV/NaoaUdXHVfWr9tBrsaScv4Dl0XwK6BCRZ0o4pwtU9UxVXWs/vg54\nRFUXAY/YjwEuwZJwXgRcDdwMYBum64FzgXOA60VkUgnnVzZ6Y5WRCi6nIRsL9TXubRHvdNV203eX\n6eQ8ig4B0XiSmoBvxLmdcuP20Fl3f7Ko0maHptoAPWUyNMlUmmf3nMiEzQAWTG2gsTbAxhLJfZeK\nvEeZiOwRkZ+JyGdE5M1YHoyq6g7A8RA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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# To obtain the number of active links, first split the network into snapshots (the .groupby() method),\n", "# Then count the number of rows corresponding to that snapshot (the .size() method).\n", "\n", "times, active_links = get_link_activity(net)\n", "plt.plot(times, active_links)\n", "\n", "plt.xlabel('Time', fontsize = 16)\n", "plt.ylabel('# active links', fontsize = 16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate SIR dynamics" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def simulate_SIR(t_max, tr, rec, network, N0):\n", " '''\n", " This functions simulates a SIR process on a temporal network\n", " \n", " Arguments:\n", " \n", " 1) t_max: number of simulation time steps; if t_max is larger than the duration of the network, \n", " periodic boundary conditions are applied. The simulation may stop earlier if the epidemics dies out.\n", " \n", " 2) tr: per-contact probability to transmit the disease from an infected individual to a susceptible one \n", " during a single time step \n", " \n", " 3) rec: probability to become recovered during a single time step\n", " \n", " 4) N0: number of initially infected individuals\n", " \n", " Output:\n", " \n", " a list containing the prevalence during each time step\n", " \n", " '''\n", " \n", " T = np.amax(network[:,0]) # get network duration (the period)\n", " node_labels = np.unique( network[:, 1:3]) # get nodes' labels\n", " \n", " state = {i: 'S' for i in node_labels} # set all nodes as susceptibles ('S')\n", " \n", " #===== Seed the infection =====#\n", " \n", " first_nodes = np.unique( network[ network[:,0] == 0 ][:, 1:3]) # select all nodes appearing in the first snapshot\n", " \n", " # Choose N0 individuals at random among the nodes that appear in the first snapshot \n", " if N0 < len(first_nodes):\n", " seeds = np.random.choice(first_nodes, size = N0, replace = False)\n", " else:\n", " seeds = first_nodes\n", " \n", " # Set the chosen nodes to infected (I)\n", " for seed in seeds:\n", " state[seed] = 'I'\n", " \n", " prevalence = [min(N0, len(first_nodes))] # This list will store the results\n", " \n", " #===== Start the simulation =====#\n", " \n", " for t in range(t_max):\n", " snapshot = network[ network[:,0] == t % T ] # select the snapshot (use t mod T in order to use periodic boundary conditions) \n", " new_infected = [] # This list will store the nodes that will become infected during this time step\n", " \n", " # loop over contacts in the current snapshot\n", " for edge in snapshot[:,]:\n", " s1 = state[ edge[1] ]\n", " s2 = state[ edge[2] ]\n", " \n", " # check if the contact is between a susceptible and an infected node\n", " if ( (s1 == 'S') and (s2 == 'I') ) or ( (s2 == 'S') and (s1 == 'I') ) :\n", " \n", " if s1 == 'S':\n", " target_node = edge[1]\n", " else: \n", " target_node = edge[2]\n", " \n", " # check if infection occurs with probability given by the transmissibility ('tr')\n", " # if infection occurs, do not set the susceptible node infected straight away\n", " # but store this information in 'new_infected'.\n", " # Also check that the susceptible node has not been infected yet!\n", " if target_node not in new_infected:\n", " if np.random.random() < tr:\n", " new_infected.append( target_node )\n", " \n", " # loop over nodes; if a node is infected, it recovers with probability given by 'rec' and its\n", " # status is set to 'R'\n", " for node, s in state.items():\n", " if s == 'I':\n", " if np.random.random() < rec:\n", " state[node] = 'R'\n", " \n", " # finally update the status of nodes that have been successfully infected\n", " for node in new_infected:\n", " state[node] = 'I'\n", " \n", " # Compute the prevalence and store it\n", " prev = len([node for node, s in state.items() if s == 'I'])\n", " prevalence.append(prev)\n", " \n", " # if there no infected halt the simulation\n", " if prev == 0:\n", " break\n", " \n", " return prevalence" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "t_max = 200 # maximum simulation time\n", "transmissibility = 0.03 # infection probability\n", "recovery = 0.05 # recovery probability\n", "N0 = 1 # initial seeds\n", "\n", "# simulate SIR dynamics and obtain prevalence\n", "prevalence = simulate_SIR(t_max, tr = transmissibility, rec = recovery,\n", " network = net, N0 = N0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,u'Time')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(prevalence)\n", "\n", "plt.xlabel('Time', fontsize = 16)\n", "plt.ylabel('Time', fontsize = 16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random reference models" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def RRM_reshuffle(network):\n", " '''\n", " This function randomly shuffles the snapshot time ordering.\n", " It therefore breaks the temporal activity as well as correlations between link activations\n", " while preserving the properties of the aggregated network.\n", " \n", " See also the paper \"Infection propagator approach to compute epidemic thresholds\n", " on temporal networks: impact of immunity and of limited\n", " temporal resolution\", by Eugenio Valdano, Chiara Poletto and Vittoria Colizza, \n", " European Physical Journal B, 2015.\n", " \n", " '''\n", " res = np.copy(network) # copy network\n", " \n", " times = np.unique(res[:,0]) # get snapshot timings\n", " times_shuffle = np.copy(times) \n", " np.random.shuffle(times_shuffle) # shuffle timings\n", " \n", " # create a 1-to-1 mapping between old and new snapshot timings\n", " old_to_new_time = {t: times_shuffle[i] for i,t in enumerate(times)}\n", " \n", " # replace old snapshot timings with the corresponding new ones\n", " for i in range(len(res)):\n", " res[i,0] = old_to_new_time[res[i,0]]\n", " \n", " return res\n", " \n", "def RRM_anonymize(network):\n", " '''\n", " This function reshuffles the identity of the nodes of each time snapshot,\n", " thus preserving activity timeline and static topology of\n", " each snapshot. It breaks all dynamic community structures and cliques.\n", " \n", " See also the paper \"Infection propagator approach to compute epidemic thresholds\n", " on temporal networks: impact of immunity and of limited\n", " temporal resolution\", by Eugenio Valdano, Chiara Poletto and Vittoria Colizza, \n", " European Physical Journal B, 2015.\n", " '''\n", " \n", " res = np.copy(network) # copy network\n", " times = np.unique(res[:,0]) # get snapshot timings\n", " \n", " for t in times:\n", " node_labels = np.unique( res[ res[:,0] == t ][:, 1:3]) # get nodes' labels\n", " node_labels_shuffle = np.copy(node_labels)\n", " np.random.shuffle(node_labels_shuffle) # shuffle node labels\n", " \n", " # create a 1-to-1 mapping between old and new node labels\n", " old_to_new_labels = {old_label: node_labels_shuffle[i] for i, old_label in enumerate(node_labels)}\n", " \n", " # replace old node labels with the corresponding new ones\n", " for i in range(len(res)):\n", " if res[i,0] == t:\n", " res[i,1] = old_to_new_labels[res[i,1]]\n", " res[i,2] = old_to_new_labels[res[i,2]]\n", " return res" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Apply randomization schemes\n", "net_reshuffle = RRM_reshuffle(net)\n", "net_anonymize = RRM_anonymize(net)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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TVAkatJb8hUvAC45E8DaosI9wHZnOaDighk9HgBCC2WasehtyJqqZVIsWqJ61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bkX3MMgfz/O3eTC9GA5sTf8+kVonjTMWq9cT48psE6af+KAI/XJxg5pL6uEdA9qD1ZBDmyahbUQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compute node and link activity and compare with original network\n", "\n", "fig, ax = plt.subplots(ncols = 2, figsize = (12, 5), sharex = True)\n", "\n", "times1, active_nodes_reshuffle = get_node_activity(net_reshuffle)\n", "times2, active_nodes_anonymize = get_node_activity(net_anonymize)\n", "\n", "ax[0].plot(times, active_nodes, lw = 3, label = 'original')\n", "ax[0].plot(times1, active_nodes_reshuffle, label = 'reshuffle')\n", "ax[0].plot(times2, active_nodes_anonymize, label = 'anonymize')\n", "\n", "#==== Now plot active links ====#\n", "\n", "times1, active_links_reshuffle = get_link_activity(net_reshuffle)\n", "times2, active_links_anonymize = get_link_activity(net_anonymize)\n", "\n", "ax[1].plot(times, active_links, lw = 3)\n", "ax[1].plot(times1, active_links_reshuffle)\n", "ax[1].plot(times2, active_links_anonymize)\n", "\n", "#==== Figure settings ====#\n", "\n", "ax[0].set_xlabel('Time', fontsize = 16)\n", "ax[1].set_xlabel('Time', fontsize = 16)\n", "\n", "ax[0].set_ylabel('# active nodes', fontsize = 16)\n", "ax[1].set_ylabel('# active links', fontsize = 16)\n", "\n", "ax[0].legend(loc = 'lower center')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# simulate SIR dynamics on random reference models \n", "prevalence_reshuffle = simulate_SIR(t_max, tr = transmissibility, rec = recovery,\n", " network = net_reshuffle, N0 = N0)\n", "\n", "prevalence_anonymize = simulate_SIR(t_max, tr = transmissibility, rec = recovery,\n", " network = net_anonymize, N0 = N0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gmle33347s2fPBnxTQl2Shr817OS2RkLcUCr7jwXKSDbXgNNd/rjwpyZJIyXOqMS5v7iK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\n6PFnvxQAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot simulation results\n", "plt.plot(prevalence, label = 'original')\n", "plt.plot(prevalence_reshuffle, label = 'reshuffle')\n", "plt.plot(prevalence_anonymize, label = 'anonymize')\n", "\n", "plt.xlabel('Time', fontsize = 16)\n", "plt.ylabel('Prevalence', fontsize = 16)\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }