hv_net(1) -- Sample a random graph with an assigned joint degree distribution ====== ## SYNOPSIS `hv_net` [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 * : 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 ``.