Multivariate Network Exploration and Presentation

In “Multivariate Network Exploration and Presentation,” authors Stef van den Elzen and Jarke J. van Wijk introduce an approach they call “Detail to Overview via Selections and Aggregations,” or DOSA. I was going to make fun of them for naming their approach after a delicious south Indian dish, but since they comment that their name “resonates with our aim to combine existing ingredients into a tasteful result,” I’ll have to just leave it there.

The DOSA approach – and now I am hungry – aims to allow a user to explore the complex interplay between network topology and node attributes. For example, in company email data, you may wish to simultaneously examine assortativity by gender and department over time. That is, you may need to consider both structure and multivariate data.

This is a non-trivial problem, and I particularly appreciated van den Elzen and van Wijk’s practical framing of why this is a problem:

“Multivariate networks are commonly visualized using node-link diagrams for structural analysis. However, node-link diagrams do not scale to large numbers of nodes and links and users regularly end up with hairball-like visualizations. The multivariate data associated with the nodes and links are encoded using visual variables like color, size, shape or small visualization glyphs. From the hairball-like visualizations no network exploration or analysis is possible and no insights are gained or even worse, false conclusions are drawn due to clutter and overdraw.”

YES. From my own experience, I can attest that this is a problem.

So what do we do about it?

The authors suggest a multi-pronged approach which allows non-expert users to select nodes and edges of interest, simultaneously see a detail and infographic-like overview, and to examine the aggregated attributes of a selection.

Overall, this approach looks really cool and very helpful. (The paper did win the “best paper” award at the IEEE Information Visualization 2014 Conference, so perhaps that shouldn’t be that surprising.) I was a little disappointed that I couldn’t find the GUI implementation of this approach online, though, which makes it a little hard to judge how useful the tool really is.

From their screenshots and online video, however, I find that while this is a really valiant effort to tackle a difficult problem, there is still more work to do in this area. The challenge with visualizing complex networks is indeed that they are complex, and while DOSA gives a user some control over how to filter and interact with this complexity, there is still a whole lot going on.

While I appreciate the inclusion of examples and use cases, I would have also liked to see a user design study evaluating how well their tool met their goal of providing a navigation and exploration tool for non-experts. I also think that the issues of scalability with respect to attributes and selection that they raise in the limitations section are important topics which, while reasonably beyond the scope of this paper, ought to be tackled in future work.

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