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I enjoyed meeting many of you at Sunbelt. During the conference, I was
struck by the attention given to longitudinal networks and other issues
related to modeling changes over time.
Those of you exploring modeling techniques in this area may be interested to
know how network researchers in other fields (operations research, for
example) extend network models to account for time. This was actually the
topic of my PhD dissertation, which (in a very small nutshell) shows how to
efficiently compute critical points in the dynamic evolution of a network,
without relying on a laborious analysis of repetitive snapshots. The
research actually was recognized with an NSF mathematics postdoctoral
fellowship in 1995, but since I decided to switch fields at that time, not
much has been done with it.
If any of you are interested to know more, here is the paper:
I think the most relevant part for this community would be up to chapter 4,
after which it gets even more technical and optimization-specific.
Bruce Hoppe, PhD
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