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Hi all,

I'd just add here that the issue of missed citations is not the structural issue here. The Clauset et al paper that Charles cites is also the primary one cited by Mason in his 
paper. However if you look at Clauset et al you will see that it is basically a review of ideas in the Jones and Handcock papers but does not make this clear. It had minor 
extensions explicitly built on this prior work. So Mason and others see the Clauset et al paper and presume that the authors of that paper have faithfully represented the 
literature. This process of "masking" of other papers in this way exacerbates the problem. The Clauset et al could not have been published in statistics because of the novelty 
issue - you do need to make it clear what is new and what is review. However it was new to the physics literature (assuming that work outside that literature is not important to 
recognize). This masking is an important process to work against especially because it distorts and weakens the overall literature.

Best,

Mark

----------------------------------------------------
Mark S. Handcock
Professor of Statistics
Department of Statistics
University of California     Phone:  (310) 817-6778
Los Angeles, CA 90095-1554.   FAX:   (206) 457-1953
Web:  www.stat.ucla.edu/~handcock
internet:  [log in to unmask]


On 2/26/12 9:20 AM, Charles Kadushin wrote:
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>
> see also:
> Clauset, Aaron, Cosma Rohilla Shalizi, and M. E. J. Newman. 2009. Power-law distributions in empirical data. arXiv:0706.1062v1 [physics.data-an].
>
>
> On 2/26/2012 11:33 AM, James Holland Jones wrote:
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>>
>> Hi Barry,
>>
>> At the risk of coming across as self-serving, I will answer this question since Mark Handcock and I have done quite a bit of work on this topic.
>>
>> They're generally right. I don't think that any human network that isn't mediated by technology actually has a power-law degree distribution. Maintaining relationships is too
>> costly. I'm also skeptical about the power-law status of most socio-technical networks, but I keep an open mind. Not surprisingly, they missed (or ignored) some work by
>> non-physicists. Mark Handcock and I noted the statistical weakness of all the power-law degree distribution work in a series of papers and provided an alternative approach (ML
>> estimation of actual probability models rather than OLS fitting of the double-log survival plot).
>>
>> Jones, J. H., and M. S. Handcock. 2003. An Assessment of Preferential Attachment as a Mechanism for Human Sexual Network Formation. Proceedings of the Royal Society of London
>> Series B-Biological Sciences. 270:1123-1128.
>>
>> Jones, J. H., and M. S. Handcock. 2003. Sexual Contacts and Epidemic Thresholds. Nature. 425:605-606.
>> Network Formation. Proceedings of the Royal Society of London Series B-Biological Sciences. 270:1123-1128.
>>
>> Handcock, M. S., and J. H. Jones. 2004. Likelihood-Based Inference for Stochastic Models of Sexual Network Evolution. Theoretical Population Biology. 65:413-422.
>>
>> Handcock, M. S., and J. H. Jones. 2006. Interval Estimates for Epidemic Thresholds in Two-Sex Network Models. Theoretical Population Biology. 70:125-134.
>>
>> In the first two, we show that the variance of the power-law exponent is greatly under-estimated by OLS (and also suggest that the same marginal power-law degree distribution can
>> represent very different networks from an epidemiological perspective). Our estimates (and 95% CIs) of the scaling exponents show that if power laws do fit sexual network data,
>> they fall off so fast (because the exponents a much greater than 2) that all the weird stuff about epidemics on power-law graphs (e.g., no epidemic threshold level of
>> transmission) don't actually apply. In the 2004 paper, we show that the best-fitting model is generally not a power law when we test against multiple models. It's a shifted
>> negative binomial. Longish tail but certainly not fat. Re Stumpf& Porter's point about the weakness mechanistic sophistication, this can be seen in the fact that so a wide
>> variety of models -- with differing underlying stochastic processes leading to the marginal degree distribution -- f!
> it!
>> about equally well. The OLS fit to the logged survival plot provides no insight into the behavior driving the evolution of the network.
>>
>> There is also a very good critique of power laws (also missed) that doesn't specifically address networks. He suggests that most power laws arise because of truncated data
>> samples drawn from exponential-like distributions, especially lognormals.
>>
>> Perline, R. 2005. Strong, Weak and False Inverse Power Laws. Statistical Science. 20 (1):68-88.
>>
>> I will present very detailed data gathered using wireless sensor networks on the degree distribution of school contact network at Sunbelt this year and show that it is not even
>> remotely power-law. The weighted degree distribution is very well fit by a normal mixture. Indeed, I think that a mixture interpretation actually works for all the supposedly
>> power-law degree distributions of social networks (e.g., sexual networks): a power law is a highly parsimonious way to fit a widely separated mixture. A negative binomial/Poisson
>> mixture, corresponding to a high-risk/main population, has at least 5 parameters (with the possibility of a shift parameter for the nb). A power-law like a Yule or Riemann
>> distribution has one. With the small samples we get from even nationally representative studies like Sex in Sweden or NSHLS, there isn't enough statistical power to show that the
>> heavily parameterized mixture model actually fits better even though the stochastic mechanism may make !
> mo!
>> re sense behaviorally.
>>
>> Allometric scaling laws are very well supported empirically. I know that there is a fair degree of controversy over their mechanistic basis however (namely, fractal geometry of
>> biological transport networks within organisms) that is not addressed in this brief note to Nature. I don't know the current status of that debate.
>>
>> Hope this helps...
>>
>> Cheers,
>> Jamie
>>
>>
>> On Feb 26, 2012, at 6:42 AM, Barry Wellman wrote:
>>
>>> ***** To join INSNA, visit http://www.insna.org *****
>>>
>>> Michael Stumpf& Mason Porter.
>>> Scienc 10Feb 2012
>>>
>>> Strongly critcizes power law modeling.
>>>
>>> Comments by more knowledgeable than me?
>>>
>>>
>>> Barry Wellman
>>> _______________________________________________________________________
>>>
>>> S.D. Clark Professor of Sociology, FRSC NetLab Director
>>> Department of Sociology 725 Spadina Avenue, Room 388
>>> University of Toronto Toronto Canada M5S 2J4 twitter:barrywellman
>>> http://www.chass.utoronto.ca/~wellman fax:+1-416-978-3963
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>>
>> --
>> James Holland Jones
>> Associate Professor, Department of Anthropology
>> Senior Fellow, Woods Institute for the Environment
>>
>> 450 Serra Mall
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>>
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