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I'm convinced that a lot of innovative ideas emerge at the borderline
between fields.
"Complex network theory" seems to have an understanding that it
belongs to complexity theory as an interdisciplinary field
rather than to physics although at the same time it forms a strand
inside of statistical physics.
A lot of claims have been made around universality of laws and for me
the question to ask is why this seems to be so important and
By the way - as Moses explained - this is not new,
cybernetics and self-organization theories had the same attitude
and have been quite successfully traveling around many different

I think that claims of universality can open up different perspectives

in fields. To give an example, if some social networks are scale-free
or small worlds what does this mean in terms of content? If it means
that one has to look to the mechanisms from which these network
are build, what the formal law of preferential attachment means in
of the social process constituting links in networks. I think that such
play around generalizations on a formal level and re-specification on a
level can be a source for new questions also for the social mechanisms

behind networks.
(see )
And sometimes the claim of a "law" as power law distributions can
provoke to
ask for deviations (see Loet Leydesdorffs remark).

By the way among the many network projects recently
a project was funded in Europe called
"Critical events in evolving networks - CREEN" ( web site

is in the making, and for a short information )
The project combines social scientists and information scientists
studying communication networks, computer scientists developing graph
algorithms and visualization and
physicists modeling dynamics on/of networks.


Dr. Andrea Scharnhorst
Netherlands Institute for Scientific Information Services (NIWI)
Joan Muyskenweg 25

Postbus 95110
1090 HC Amsterdam
The Netherlands
Tel: +20 4628 670

>>> "Carter T. Butts" <[log in to unmask]> 3-2-2005 12:36:38 >>>
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Bettina Hoser wrote:

> But about the general laws: physicists have a very long tradition of
> finding laws in nature that did not look as though they have
> in common. Later research found they had thouhg! So maybe it would
> a very good idea to let the physisicst look for general laws, while
> the sociologists use the methods to gain mor insight into the
> sociological questions.

Hmm.  But, indeed, there is no reason that sociologists (or anyone
cannot find general laws, assuming such laws are there to be found.  I
think that the real question here is not one of physicists vs.
sociologists, or lawfulness vs. unpredictability.  The question is one
of good science vs. bad science: does the particular theory being
proposed in this case actually predict the phenomena for which the
theory allegedly holds?  Not having read the original piece, I cannot
speak to that -- the short description of the result posted earlier
_sounds_ inconsistent with known observations, but this might be due
miscommunication.   (It would seem productive to get a better handle
what is being claimed, prior to having a debate over the truth or
falsity of those claims.)

Abstract debates over the applicability of the theories/methods of
physics (or biology, computer science, etc.) to social science are
amusing, but I'm not aware that anything useful has ever come of them.
Talk is cheap: if a genuine advance can result from cross-application
ideas, then the way to prove it is to actually make the advance.  This
has happened before (e.g., the work of Rashevsky's group circa 1950,
the influence of Besag's biostatistical work on modern ERG models),
it will doubtless happen again.  Nevertheless, I think the field is
better served by a consideration of specific proposals than by
arguments for/against the importation of physical or other ideas.

(I'm surprised, in this regard, that no one here has pointed out that
ERG/p* models are essentially thermodynamic in character.  Recognizing
this connection greatly facilitates the interpretation of some of the
more unusual behaviors of these models, e.g., phase transitions and
degeneracy.  These issues are being explored by a number of people on
this list, at least some of whom are explicitly incorporating
statistical mechanical arguments/results into their work.
Cross-fertilization works well in this case, because there is a strong
substantive motivation for the modeling framework.)


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