3 ms·
Placing infected individuals in the top rows, ranking uninfected individuals by probability of infection, lay out the network. Overall shape of network might gi
by wjrl 12y ago
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.
- Fomite 12y agoIt'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.
- wjrl 12y agoOf course, YMMV, but the Stanford web network (http://www.biofabric.org/gallery/index.html#Stanford 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 https://github.com/ajmazurie/biofabric, though I have not tried it myself.
- deleted 12y ago[deleted]