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Last active August 29, 2015 14:00
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Preferential attachment algorithm with Guido's graph notation
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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"DD2399, question 4"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# For this implementation, I am using Guido's way:\n",
"# https://www.python.org/doc/essays/graphs/\n",
" \n",
"graph = {'A': ['B', 'C'],\n",
" 'B': ['C', 'D'],\n",
" 'C': ['D'],\n",
" 'D': ['C'],\n",
" 'E': ['F'],\n",
" 'F': ['C']\n",
" }\n",
"\n",
"# IMPORTANT implementation detail: for simplicity, ignore arcs (1: [0], 0: [1]), consider them as plain, undirected edges 1: [0]."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"from collections import defaultdict, Counter\n",
"\n",
"def prob(G):\n",
" \"\"\" Given a graph G with Guido's representation, returns a \"popular\" node, as required by BA.\n",
" \"\"\"\n",
" popular = zip(G.keys(), (len(x) for x in G.values()))\n",
" # pick uniformly from repeated nodes. Those more often repeated have more chances to get elected.\n",
" # i.e, given nodes: [1,1,1,1,2,3,4,5], those labeled as \"1\", have more chances to be returned. \n",
" return random.choice(list(Counter(dict(popular)).elements()))\n",
"\n",
"def bara(n, m, steps=False):\n",
" \"\"\" BA algoritm, given n nodes and m edges to be attached, as with the NetworkX API.\n",
" \"\"\"\n",
" G = defaultdict(list)\n",
" nodes = range(n)\n",
" # Just for generating more interesting start conditions, nothing to do with prefattach\n",
" random.shuffle(nodes)\n",
" # Put first two nodes in the graph\n",
" G[nodes.pop()] = [nodes.pop()]\n",
"\n",
" while nodes:\n",
" # See the graph being built step by step\n",
" if steps: print G, prob(G)\n",
"\n",
" node = nodes.pop()\n",
" for i in range(m): # connect each edge \"m\" from new node to the graph \"G\"\n",
" popu_node = prob(G)\n",
" if node not in G[popu_node]: # no repeated nodes\n",
" G[popu_node].append(node)\n",
" else:\n",
" G[node].append(popu_node)\n",
" return G\n",
"\n",
"bara(3, 1), bara(4, 2), bara(8, 2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"(defaultdict(<type 'list'>, {1: [2, 0]}),\n",
" defaultdict(<type 'list'>, {0: [3, 2], 2: [0], 3: [1, 0]}),\n",
" defaultdict(<type 'list'>, {1: [0, 6], 2: [4], 3: [6], 4: [6, 2], 5: [7], 6: [1, 3, 7, 4], 7: [6, 5]}))"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The above results resemble graphs generated with NetworkX, such as the following.\n",
"# Both follow the power law (http://en.wikipedia.org/wiki/Power_law).\n",
"\n",
"# With the notation indicated above, the graph below can be wrote done as:\n",
"{0: [1, 5, 3, 2], 1: [0], 2: [0, 6, 7], 3: [0, 4], 4: [3], 5: [0]}"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image\n",
"Image(filename='barabasi.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
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Nlovu3btnxX8flfn+9re/MXHiRN5++21OPPHEsONIWckCIumgrV69er+F7uXl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\n",
"prompt_number": 18,
"text": [
"<IPython.core.display.Image at 0x106453fd0>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Just for the fun of it, one can generate BA and ER graphs on ipython notebook with NetworkX :D\n",
"\n",
"%pylab inline\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"\n",
"G = nx.Graph()\n",
"\n",
"ba = nx.barabasi_albert_graph(8, 1)\n",
"er = nx.erdos_renyi_graph(15, 0.15)\n",
"\n",
"nx.draw(ba)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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fG9itGz9v2ZJl+aYCyUDK339OJP0HQOYrM/92zz2s+fZbqlatSlRUFOfOnbv5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\n",
"text": [
"<matplotlib.figure.Figure at 0x105e6a750>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nx.draw(er)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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e2efPn3P06NE3rdrEM/14enrSoEGDTHX8EiIrSfgaiX379uHdsSOBERFJxl1V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\nEsgbF8dzMzMOWlkxMCGBXp9+yoBhw1L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"text": [
"<matplotlib.figure.Figure at 0x105e41ed0>"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
}
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