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Chapter 5.5, exercise 6, #3: compute WS clustering coefficient and characteristic length on a BA model graph.
author Brian Neal <bgneal@gmail.com>
date Thu, 10 Jan 2013 19:24:02 -0600
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35:10db8c3a6b83 36:305cc03c2750
1 """Chapter 5.5, exercise 6 in Allen Downey's Think Complexity book.
2
3 3. Use the BA model to generate a graph with about 1000 vertices and compute the
4 characteristic length and clustering coefficient as defined in the Watts and
5 Strogatz paper. Do scale-free networks have the characteristics of
6 a small-world graph?
7
8 """
9
10 from ch5ex6 import BAGraph
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12 g = BAGraph(5, 5)
13
14 for i in xrange(1000):
15 g.step()
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17 g.set_edge_length(1)
18 print "Clustering coefficient:", g.clustering_coefficient()
19 print "Characteristic length:", g.big_l3()