{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from webweb import Web\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a SBM network with 3 groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose sizes of groups\n",
    "n1 = 40\n",
    "n2 = 40\n",
    "n3 = 70\n",
    "n = n1+n2+n3\n",
    "\n",
    "# group memberships, b\n",
    "b = np.array([0]*n1 + [1]*n2 + [2]*n3)\n",
    "\n",
    "# group affinity matrix\n",
    "omega = [ [0,0.05,0],\n",
    "          [0.05,0.2,0.05],\n",
    "          [0,0.05,0]]\n",
    "\n",
    "# SBM\n",
    "edge_list = []\n",
    "for i in range(1,n):\n",
    "    for j in range(i):\n",
    "        if np.random.rand() < omega[b[i]][b[j]]:\n",
    "            edge_list.append([i,j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Display using webweb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "web = Web(title='sbm_demo')\n",
    "web.networks.sbm(\n",
    "    # assign its edgelist\n",
    "    adjacency=edge_list,\n",
    "    # give it the community metadata\n",
    "    metadata={'community': \n",
    "        {\n",
    "            'values' : list(b),\n",
    "            'type' : 'categorical',\n",
    "        },\n",
    "    }\n",
    ")\n",
    "web.display.colorBy = 'community'\n",
    "web.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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