hv_net - Sample a random graph with an assigned joint degree distribution
hv_net graph_in [SHOW]
hv_net samples a random graph whose joint degree distribution is
equal to that of another graph provided as input, using the
hidden-variable model proposed by Boguna ans Pastor-Satorras.
File containing the edge list of the existing graph. If equal to '-' (dash), read the edge list from STDIN.
If the second parameter is equal to SHOW, the program prints on
STDERR the hidden variable and actual degree of each node.
Let us assume that we want to create a graph whose joint degree
distribution is equal to that of the graph contained in
AS-20010316.net (i.e., the graph of the Internet at the AS level in
March 2001). We can use the command:
$ hv_net AS-20010316.net > AS-20010316.net_rand
which will sample a random graph with the same joint-degree
distribution and will save its edge list in the file
AS-20010316.net_rand (notice the STDOUT redirection operator
>). Additionally, we can also save the values of the hidden
variables and actual degrees of the nodes by specifying SHOW as a
second parameter:
$ hv_net AS-20010316.net SHOW > AS-20010316.net_rand 2>AS-20010316.net_rand_hv
In this case, the file AS-20010316.net_rand_hv will contain the
values of the hidden variable of each node and of the actual degree of
the node in the sampled graph, in the format:
h1 k1
h2 k2
....
conf_model_deg(1), conf_model_deg_nocheck(1)
M. Boguna and R. Pastor-Satorras. "Class of correlated random networks with hidden variables". Phys. Rev. E 68 (2003), 036112.
V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles, Methods and Applications", Chapter 7, Cambridge University Press (2017)
V. Latora, V. Nicosia, G. Russo, "Complex Networks: Principles, Methods and Applications", Appendix 14, Cambridge University Press (2017)
(c) Vincenzo 'KatolaZ' Nicosia 2009-2017 <v.nicosia@qmul.ac.uk>.