sune lehmann

Link communities R package

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Sune Lehmann
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A while ago, I wrote about Rob Spencer over at Scaled Innovation‘s implementation of the algorithm for detecting link communities. Today, I am happy to report on another exciting development for the alorithm. Alex Kalinka from the Tomancak lab at the Max Plank Institute (MPI-CBG) has written a great implementation in R, called linkcomm. It is now up on CRAN:

http://cran.r-project.org/web/packages/linkcomm/index.html

While everything is excellent, the graphics are particularly beautiful – much prettier than our own visualizations – check out the colored link dendrogram plot (from the CRAN website)

And the spatial network layout options are great as well; the various community visualizations are simple, elegant, and very pretty:

The panel on the left shows a 'Spencer circle' layout, while the panel on the right shows a Fruchterman-Reingold layout. From the linkcomm documentation.

In addition, there are many neat features. For example, linkcomm allows you to visualize sub-communities by themselves. Alex has also published an Application Note in Bioinformatics about the implementation, so take a look if you’re interested:

http://bioinformatics.oxfordjournals.org/content/early/2011/05/19/bioinformatics.btr311.abstract (open access).

We also link to the package from our link communities download page.

Comments

2 responses to “Link communities R package”

  1. Finn Årup Nielsen Avatar
    Finn Årup Nielsen

    I am interested in radial network visualization. I haven’t made GraphViz twopi work particular well. I am looking for a program that does a better layout than the bullseye visualization I have done:

    I wonder about Spencer layout I haven’t heard of that before.

    Reply
    1. Sune Lehmann Avatar
      Sune Lehmann

      Hey Finn,

      Thanks for the comments.

      Check out http://www.win.tue.nl/~dholten/papers/bundles_infovis.pdf for super cool radial layouts (esp Fig 13.)

      The ‘Spencer’ layout is the one Rob Spencer came up with for link clustering see https://sunelehmann.com/2010/11/03/visualizing-link-communities/ for details.

      Reply

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