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for a recent study, I built a similar network consisting of almost
15,000 authors and 59,000 coauthorship relations. I gathered the data
from the arXiv repository, restricting the focus on those papers with
"networks" in the title or abstract up to May 2014 (this includes papers
about "neural networks", for instance, but they can be easily filtered
out if required). Possible metadata include the category to which each
author submitted a paper (that I used to build a multiplex network, at
the very end), as well as title, publication year, etc. Categories
You can find the dataset in a dedicated web page
(http://deim.urv.cat/~manlio.dedomenico/data.php) or download it
If it can be of any help, do not hesitate to contact me for further
details (for instance, not all metadata is released with the network,
but I will be happy to share it upon request).
On 30/12/16 14:29, Dan Suthers wrote:
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> Dear Network Science / SNA community,
> You are probably aware of the "Coauthorships in network science"
> network posted on Mark Newman's site
> (http://www-personal.umich.edu/~mejn/netdata/) as
> http://www-personal.umich.edu/~mejn/netdata/netscience.zip and many
> other places.
> I have found this network to be very useful as a demonstration example
> when teaching network analytics. It is small enough that most analytic
> examples can be run as part of a class demonstration, for example for
> units on random vs. natural graphs, node centralities, degree
> correlation, community detection, etc. Also it has the bonus that in
> the process the students learn the names of various important authors.
> Therefore I have used it as one of several graphs throughout the
> semester in my course on network science.
> However, it is now 10 years old. (It also emphasizes physics authors
> more than sociology.) Does anyone know of an updated version or
> comparable network that would serve this dual instructive purpose?
> It would be especially nice to have a version of this network that
> also includes node attributes (of any relevant sort), as this would
> extend the range of methods that could be illustrated with the
> network, as well as the pedagogical utility for learning about the
> Thanks in advance for any suggestions,
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