2.1 KiB
hv_net(1) -- Sample a random graph with an assigned joint degree distribution
SYNOPSIS
hv_net <graph_in> [SHOW]
DESCRIPTION
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.
PARAMETERS
-
<graph_in>: File containing the edge list of the existing graph. If equal to '-' (dash), read the edge list from STDIN.
-
SHOW: If the second parameter is equal to
SHOW, the program prints on STDERR the hidden variable and actual degree of each node.
EXAMPLES
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
....
SEE ALSO
conf_model_deg(1), conf_model_deg_nocheck(1)
REFERENCES
-
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)
AUTHORS
(c) Vincenzo 'KatolaZ' Nicosia 2009-2017 <v.nicosia@qmul.ac.uk>.