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As a systems biologist and web developer, I say major thumbs up for the presentation of the idea, but a thumbs sideways for the actual visualization method. I'

As a systems biologist and web developer, I say major thumbs up for the presentation of the idea, but a thumbs sideways for the actual visualization method. I'm not sure this makes biology clearer. Seems like rearranging deck chairs.

Fomite 12y ago
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!"
- 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 ago
    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.
    - wjrl 12y ago
      Of 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]
wjrl 12y ago
I think the protein-protein interaction network on the BioFabric home page (www.BioFabric.org) shows how one can recognize nodes with similar neighborhoods at the global level. Being able to spot these patterns is unique. Can also do network comparisons, and show clearer cluster structure. Check out the Oct 2012 paper in BMC Bioinformatics.

Fomite 12y ago

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!"

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 ago
  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.
  - wjrl 12y ago
    Of 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]

wjrl 12y ago

I think the protein-protein interaction network on the BioFabric home page (www.BioFabric.org) shows how one can recognize nodes with similar neighborhoods at the global level. Being able to spot these patterns is unique. Can also do network comparisons, and show clearer cluster structure. Check out the Oct 2012 paper in BMC Bioinformatics.