Goodbye to hairballs?

You probably have seen the hairballs resulting from a force-directed layout of complex biological networks. What do they tell you? Well, that the networks are rather complex. But for much more detailed analysis the classical visualizations are actually quite useless. The hiveplot  is an attempt to provide

“A scalable, computationally fast, and straight-forward network visualization method that makes possible visual interpretation of network structure and evolution.”

A laudable goal, if it works in practice for you and your data – check it out. In addition there is an R package available for creating hive plots in 2D and 3D called HiveR.

Also see Krzywinski M, Birol I, Jones S, Marra M (2011). Hive Plots — Rational Approach to Visualizing Networks. Briefings in Bioinformatics (doi: 10.1093/bib/bbr069).

Thanks to Lucy Colwell for the hint!

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  1. #1 by cistronic on 2012/02/22 - 13:34

    As a note to myself: Another visualisation technique I found rather intriguing (although not directly related)

    see Collins, Christopher; Penn, Gerald; Carpendale, Sheelagh. Bubble Sets: Revealing Set Relations over Existing Visualizations. IEEE Transactions on Visualization and Computer Graphics (Proceedings of the IEEE Conference on Information Visualization (InfoVis ’09)), 15(6): November-December, 2009.

  2. #2 by cistronic on 2012/02/22 - 13:44

    And another one (featured in “Visualization of communities in networks,” Amanda L. Traud, Christina Frost, Peter J. Mucha, and Mason A. Porter, Chaos 19, 041104 (2009) ):

    providing combinations of Fruchterman-Reingold and Kamada-Kawai Algorithms
    for the placement of / placement of nodes within communities

  3. #3 by cistronic on 2012/10/06 - 22:42

    Sigma.js Cleans up Hairball Network Visualizations

  4. #4 by Bill Longabaugh on 2012/11/13 - 08:31

    For another alternative to hairballs, consider a new software tool that I just released named BioFabric, available at: It deals with the problem by representing nodes as horizontal lines instead of as points, thereby creating scalable and unambiguous network visualizations.

further hints, constructive criticism, questions, praise

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