It is a well known model, whose properties are by now quite well underst= ood. It can be used to generate networks of any size. The degree distributi= on is Poissonian for any value of the rewiring probability (except in the e= xtreme case of rewiring probability zero, for which all nodes have equal de= gree), the clustering coefficient is tunable.

=20It is usually studied to explore networks with tunable values for the av= erage shortest path between pairs of nodes and the clustering coefficient. = Networks with small values for the average shortest path and large values f= or the clustering coefficient are supposed to simulate social networks.

= =20The algorithm requires three inputs: the number of nodes of the network,=
the number k of initial neighbors of each node on the two sides of the net=
work (the initial configuration is a ring of nodes) and the probability of =
rewiring the edges (which is a real number between 0 and 1). The network is=
built following the original prescription of Watts and Strogatz, i.e. by s=
tarting from a ring of nodes each connected to the k nodes to their right/l=
eft and by rewiring each edge with the specified probability.

The algor=
ithm runs in a time O(kn), where n is the number of nodes of the network.=20

As an exercise, one could try to build a small world network with 1000 n= odes, 10 initial neighbors per side and a rewiring probability of 0.01. Aft= er the network file has been produced, one could select it and apply on it = the two algorithms on the average shortest path and the clustering coeffici= ent, to verify that the former is small and the latter is still relatively = large.

=20The algorithm was implemented and documented by S. Fortunato, integrated= by S. Fortunato and W. Huang. For the description we acknowledge Wikipedia= .

=20Watts, D.J., Strogatz, S.H.(1998) Collective dynamics of 'small-world' networks. Nature 393:440-442= .

=20- =20
- Source Code =20

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