***** To join INSNA, visit http://www.sfu.ca/~insna/ ***** Vaughan, As I indicated to you, this method is described by Duncan Watts in his 1999 AJS paper. More specicially, you don't need to generate random networks to know their path length or clustering coefficient. The approximations for path length can be computed as follows: Lrandom=ln(n)/ln(k) where n is the number of nodes and k is a number of ties in the network Approximation for clustering coefficient is computed as Crandom=k/n Again, for more comprehensive review, I suggest you read: Watts, D. 1999 "Networks, dynamics and the small world phenomenon." American Journal of Sociology, 105: 493-527 Andrew -----Original Message----- From: Vaughan [mailto:[log in to unmask]] Sent: Tue 11/18/2003 6:03 AM To: [log in to unmask] Cc: Subject: Random network generation ***** To join INSNA, visit http://www.sfu.ca/~insna/ ***** Hello everyone, I would like to generate a random network, with a similar number of ndoes and connection to the network I have created from field data, to compare the distance and clustering co-efficient of each. Not unlike the the method in http://arxiv.org/pdf/cond-mat/0307439 My network created from field data has no isolated nodes. However, when I ask Pajek to create a network with the same number of nodes and connections I get plenty of isolates, which apparently will not make a valid clustering co-efficient or distance comparison. Andrew Shipilov kindly suggested to my that it is possible to generate distance and clustering coefficients purely from knowing the number of nodes and connections I wish to use, although I am having trouble tracking down this method. Can anyone suggest a way of either generating a random network with a specified number of nodes and connections that has no isolates or doing the above calculation ? Many thanks, Vaughan Bell _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.sfu.ca/~insna/). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message. _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.sfu.ca/~insna/). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.