.\" generated with Ronn/v0.7.3
.\" http://github.com/rtomayko/ronn/tree/0.7.3
.
.TH "HV_NET" "1" "September 2017" "www.complex-networks.net" "www.complex-networks.net"
.
.SH "NAME"
\fBhv_net\fR \- Sample a random graph with an assigned joint degree distribution
.
.SH "SYNOPSIS"
\fBhv_net\fR \fIgraph_in\fR [SHOW]
.
.SH "DESCRIPTION"
\fBhv_net\fR 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\.
.
.SH "PARAMETERS"
.
.TP
\fIgraph_in\fR
File containing the edge list of the existing graph\. If equal to \'\-\' (dash), read the edge list from STDIN\.
.
.TP
SHOW
If the second parameter is equal to \fBSHOW\fR, the program prints on STDERR the hidden variable and actual degree of each node\.
.
.SH "EXAMPLES"
Let us assume that we want to create a graph whose joint degree distribution is equal to that of the graph contained in \fBAS\-20010316\.net\fR (i\.e\., the graph of the Internet at the AS level in March 2001)\. We can use the command:
.
.IP "" 4
.
.nf

    $ hv_net AS\-20010316\.net > AS\-20010316\.net_rand
.
.fi
.
.IP "" 0
.
.P
which will sample a random graph with the same joint\-degree distribution and will save its edge list in the file \fBAS\-20010316\.net_rand\fR (notice the STDOUT redirection operator \fB>\fR)\. Additionally, we can also save the values of the hidden variables and actual degrees of the nodes by specifying \fBSHOW\fR as a second parameter:
.
.IP "" 4
.
.nf

    $ hv_net AS\-20010316\.net SHOW > AS\-20010316\.net_rand 2>AS\-20010316\.net_rand_hv
.
.fi
.
.IP "" 0
.
.P
In this case, the file \fBAS\-20010316\.net_rand_hv\fR 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:
.
.IP "" 4
.
.nf

     h1 k1
     h2 k2
     \.\.\.\.
.
.fi
.
.IP "" 0
.
.SH "SEE ALSO"
conf_model_deg(1), conf_model_deg_nocheck(1)
.
.SH "REFERENCES"
.
.IP "\(bu" 4
M\. Boguna and R\. Pastor\-Satorras\. "Class of correlated random networks with hidden variables"\. Phys\. Rev\. E 68 (2003), 036112\.
.
.IP "\(bu" 4
V\. Latora, V\. Nicosia, G\. Russo, "Complex Networks: Principles, Methods and Applications", Chapter 7, Cambridge University Press (2017)
.
.IP "\(bu" 4
V\. Latora, V\. Nicosia, G\. Russo, "Complex Networks: Principles, Methods and Applications", Appendix 14, Cambridge University Press (2017)
.
.IP "" 0
.
.SH "AUTHORS"
(c) Vincenzo \'KatolaZ\' Nicosia 2009\-2017 \fB<v\.nicosia@qmul\.ac\.uk>\fR\.