This was my thought as well - I work with large contact networks for epidemiology, and while getting away from hairballs would be good in theory, I'm not seeing how a figure built using this technique will make me go "Oh! Yes, now I see it!"
Placing infected individuals in the top rows, ranking uninfected individuals by probability of infection, lay out the network. Overall shape of network might give clues to network structure? I find that with contact networks, dynamic networks (links appear and disappear) tend to have problems with force-directed layouts bouncing around a lot.
It's possible. My issue is I'm not sold that in networks of any size (the Le Mis network is pretty small) that this doesn't end up in a similarly difficult to interpret mess. I may try it out, but the initial demo didn't trigger much beyond "Neat".
But to be honest, I don't tend to work in .sif format networks, and I don't have time tonight to try to get something I actually use in that format.
Of course, YMMV, but the Stanford web network (http://www.biofabric.org/gallery/index.html#Stanford) has 2.3 million edges but you can glean structure from it. You can visually estimate things like the network radius and 90-percentile effective diameter from the global view.
There is a very simple R implementation that takes igraph networks as input. There is also a simple Python version from A. Mazurie at https://github.com/ajmazurie/biofabric, though I have not tried it myself.
