The algorithm builds the histogram of the values of the degree of all no= des, which will be delivered in the two output files.

=20The network to analyze must be undirected, otherwise there are no specia= l constraints.

=20Basic analysis tool, not particular for special disciplines or problems.=

=20The algorithm requires two inputs, the file where the edges of the netwo=
rk are listed and the number of points one wishes to have in the binned dis=
tribution described below. A first read-in of the inputfile will set the va=
lues of the number of nodes and edges of the network. In the second read-in=
the degrees of all nodes will be calculated. Then the distribution is calc=
ulated.

The program generates two output files, corresponding to two di=
fferent ways of partitioning the interval spanned by the values of degree. =
In the first output, the occurrence of any degree value between the minimum=
and the maximum is estimated and divided by the number of nodes of the net=
work, so to obtain the probability: the output displays all degree values i=
n the interval with their probabilities.

The second output gives the

This technique is particularly suitable to study =
skewed distributions: the fact that the size of the bins grows large for la=
rge degree values compensates for the fact that not many nodes have high de=
gree values, so it suppresses the fluctuations that one would observe by us=
ing bins of equal size. On a double logarithmic scale, which is very useful=
to determine the possible power law behavior of the distribution, the poin=
ts of the latter will appear equally spaced on the x-axis.

The program =
runs in a time O(m), m being the number of edges of the network.

A simple application of this algorithm could be to calculate the degree = distribution of networks created by the modeling algorithms of the NWB. For= instance, the network file can be created through the Barabasi-Albert mode= l.

=20- =20
- Source Code =20

The algorithm was implemented and documented by S. Fortunato, integrated= by S. Fortunato and W. Huang.

=20Albert, R., and Barabasi, A.-L. (2002) Statistical mechanics of complex networks. Review of Modern Phys= ics 74:47-97.

=20Newman, M.E.J. (2003) Th= e structure and function of complex networks. SIAM Review 45:167-256.=20

Pastor-Satorras, R., Vespignani, A. (2004) Evolution and Structure of th= e Internet. Cambridge University Press.

=20Boccaletti, S., Latora, V., Moreno, Y.,Chavez, M., Hwang, D.-U. (2006) <= a href=3D"http://www.ct.infn.it/%7Elatora/report_06.pdf" class=3D"external-= link" rel=3D"nofollow">Complex networks: Structure and dynamics. Physic= s Reports 424: 175-308.

=20
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