Oh the sheer beauty of networks…

I could spend days surfing the 777 different projects that the visualcomplexity project has gathered, some of them amaze me because of what they are about, more of them just because they are so beautiful. Some examples, just to get you started

Nike city runs NYC

“Nike+ involves the placement of a sensor underneath the foot bed of your Nike running shoe in order to collect data about where you’ve run, how long it took and where you can improve over time – since each individual run becomes part of a collective historical database. Even though Nike+ website already gives individual users a variety of features to make sense of their personal data, the collective analysis of this growing database is remarkably promising.

The interactive collective YesYesNo developed an installation for Nike’s retail stores to visualize a year’s worth of runs uploaded to the Nike+ website. With custom software, the installation plays back runs throughout three cities: New York, London and Tokyo. The runs showed tens of thousands of peoples’ runs animating the city and bringing it to life. The software visualizes and follows individual runs, as well as showing the collective energy of all the runners, defining the city by the constantly changing paths of the people running in it.”

Visualizing Databases Stanford
“Using the visualization tool Gephi, Elijah Meeks has produced a series of experiments depicting databases in diverse styles. The images show here are mapping the top contributors to the Catalogue of Life and their associated species, references and databases.

As Elijah states: “While it could be argued that all databases can be devolved into graph databases, and as such all databases are graphs and therefore networks in the most pure sense, I think that there’s something more practical at play here: the importance of network visualization for database aesthetics. Summaries and statistics drawn from within the structure of the database are not enough. If there is to be any real grappling with the database as an culturally-embedded construct, then it has to be done in a manner that reveals the data, the model and the population simultaneously.”

Stephanie Posavec Writing without words Kerouac
“Writing Without Words, by Stephanie Posavec is a series of striking visualizations exploring the differences in writing style between authors of various modern classics. The images shown here are a visualization of Part One from the book On the Road by Jack Kerouac. In this piece, entitled Literary Organism, each literary component was divided hierarchically into even smaller parts – Part, Chapters, Paragraphs, Sentences, and ultimately Words, the smallest branch in the diagram. Stephanie also created different colors to distinguish the eleven thematic categories she created for the entirety of On the Road. Some categories include: Social Events & Interaction, Travel, Work & Survival, and Character Sketches, among others.

This is how NOTCOT describes Stephanie’s work:”The maps visually represent the rhythm and structure of Kerouac’s literary space, creating works that are not only gorgeous from the point of view of graphic design, but also exhibit scientific rigor and precision in their formulation: meticulous scouring the surface of the text, highlighting and noting sentence length, prosody and themes, Posavec’s approach to the text is not unlike that of a surveyor. And similarly, the act is near reverential in its approach and the results are stunning graphical displays of the nature of the subject. The literary organism, rhythm textures and sentence drawings are truly gorgeous pieces.”

From tweet to action: Who moves social movements on twitter?

People (boxes) who tweet and core words (bubbles) they use

The fact that today’s social movements, from Occupy Wall Street to the Arab Spring, rely so heavily on twitter and similar communication tools, pose an amazing chance for researchers and other curious people who want to understand who moves these movements. The other day I discussed with a friend what kind of networks you want to look at to better understand this and I’d propose three different kinds: People networks, semantic networks and two-mode people/semantic networks.

People networks are the easy intuitive ones: Who follows whom? Who re-tweets whom? Looking at this will help you understand who the leaders, boundary spanners, broad-casters are.  Most likely, for an issue that manages the step from tweet to action successfully, you will look at a core-periphery structure, with a small inter-connected core (who might also communicate regularly outside of twitter) and a large periphery of followers, who are less inter-connected but look at the core for calls to action and thought leadership. Over time, different clusters might pop up as their own sub-cores or even take over from those initially starting the debate.

Semantic networks look at which words appear together in the same document (a document could be a single tweet, a string, all tweets from one person, whichever works). This can tell you something about the discourse around your issue: Is it just one large well connected issue or are there different schools of thought (more moderate and more radical for example or more philosophical versus more pragmatic and logistics oriented)? You might see that things evolve over time, for example it might be that the movement starts out united behind one cause (“Let’s overthrow the government!”) and after that is achieved, the debate disintegrates in many different camps (moderate and radical islamists, market oriented democrats, socialists etc.).

And to really understand how this development of the debate and the connections between the tweeters hang together, you want to look at two-mode networks. But I have to warn you, they are the least intuitive. In a two mode-network you look at two different categories of things, for example people and words and how they connect to each other. So, there are no direct links within one category (no people-to-people links or word-to-word links). This picture shows you: Who uses which words? Who is connected by being part of the same discourse (even if they have no direct link to each other)?

By looking at all three of these together, you can see who the leaders are, what their role (content) in the movement is and how that develops over time. And if you can compare either different incidents or different points in time, you will learn something about the network structures that are best suited to lead from tweet to action.