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***** To join INSNA, visit http://www.sfu.ca/~insna/ ***** One further plea for recognition of previous work here. The "preferential attachment model" is what mathematical epidemiologists call "proportional mixing". It was the starting point for epidemic modeling in the mid 1980s. Prior work had milked this assumption for all it was worth to get analytical solutions to epidemic models. But there was a small body of work that began to hack away at the much harder problem of getting solutions when mixing was not proportional. Not much in the way of analytic solutions was found, so most of the applied research used simulation instead. For the people working in this area, the idea of deriving the properties of transmission systems under proportional mixing seems like a real blast from the past. Of course it can be done, it's just not clear why you would want to. At least, not if you were interested in understanding the population dynamics of sexually transmitted infections. In that context, the assumption is just wrong, and it matters. On Tue, 28 Jan 2003, Mark Newman wrote: > ***** To join INSNA, visit http://www.sfu.ca/~insna/ ***** > > There are two issues here: one is failure to give credit where it's due > and the other is the actual validity of the work. I'm just finishing up > a lengthy review article on recent work on networks by mathematicians > and physicists, and although I thought I knew this literature quite well > before I started, I have learned a lot by reading up for the review. I > agree completely that people have in some cases failed to give credit > for earlier innovations, and this is bad. But it would be a mistake to > dismiss this work out of hand. There is a great deal there that would > be of interest to all of us. > > In particular response to Mark Handcock's post about "scale-free > networks", I think it would certainly be a mistake to claim that the > physics models, like the "preferential attachment" models, are complete > models of the structure of networks. Of course there are many different > processes going on in network formation, most of which are absent from > these models. Therefore, if one compares these simple models to > sociometric data, it's virtually certain they won't match up, and Mark's > work demonstrates this elegantly. This however doesn't make the models > useless. There's much to be learned from them, even if they are > incomplete (or maybe even plain wrong). At the very least, they've > stirred up a whole new community to get interested in network ideas, and > surely that can't be all bad. > > Mark Newman. > > _____________________________________________________________________ > 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. > **************************************************************** Blumstein-Jordan Professor of Sociology and Statistics Department of Sociology Box 353340 University of Washington Seattle, WA 98195-3340 Phone Numbers: Office: (206) 685-3402 Dept Office: (206) 543-5882 Fax: (206) 616-2093 email: [log in to unmask] _____________________________________________________________________ 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.