Topology Mapping for Analytics

Seems Vincent Granville has been thinking similarly lately.  See his article and code in Data Science Central:   A little known component that should be part of most data science algorithms. Beware, this has a general introduction, and then gets technical with R code. And goes further:

" ...  This is a component often missing, yet valuable for most systems, algorithms and architectures that are dealing with online or mobile data, known as digital data: be it transaction scoring, fraud detection, online marketing, marketing mix and advertising optimization, online search, plagiarism and spam detection, etc. .... I will call it an Internet Topology Mapping. It might not be stored as a traditional database (it could be a graph database, a file system, or a set of look-up tables). It must be pre-built (e.g. as look-up tables, with regular updates) to be efficiently used. ... "

I came to think of this when I experimented with the free, open source GePhi network management system and reported on it here. Though the most common network we might work topologically is the Internet, or a social network. These are not the only networks, things like influence maps come to mind.     Have not thought how R in particular links with GePhi, but exploring.  I have a real application in mind. Any thoughts out there?