{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore Random Graphs Using NetworkX" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we build a simple UI for exploring random graphs with [NetworkX](http://networkx.github.io/)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "from ipywidgets import interact" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import networkx as nx" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# wrap a few graph generation functions so they have the same signature\n", "\n", "def random_lobster(n, m, k, p):\n", " return nx.random_lobster(n, p, p / m)\n", "\n", "def powerlaw_cluster(n, m, k, p):\n", " return nx.powerlaw_cluster_graph(n, m, p)\n", "\n", "def erdos_renyi(n, m, k, p):\n", " return nx.erdos_renyi_graph(n, p)\n", "\n", "def newman_watts_strogatz(n, m, k, p):\n", " return nx.newman_watts_strogatz_graph(n, k, p)\n", "\n", "def plot_random_graph(n, m, k, p, generator):\n", " g = generator(n, m, k, p)\n", " nx.draw(g)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3WdUVNfXBvAHFel9ZuhVUFSUZiwoFuwaNZZYEjso4t+Y\n", "GGuMJtYkNqyo2CuoUexGY+8aBSWiURFE7FIEVGCAufv9YOQNEXAaM2r2b61ZC2bOPWdfQPc9956i\n", 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