Image: Somelabs, University of Washington

This is a 3D-printed model of one man’s social network. It’s his Twittersphere, to be exact. And you too can polymerize your web of friends and family and celeb follows and politically polarizing high school classmates, and you can do it for $20. If you know how to code. 

Jeff Hemsley, a phD student at the University of Washington, knows how to code.  

“I am constantly experimenting with different ways to represent and explore social network data with R,” he writes in a blog post explaining his creation. Indeed, “the underlying idea is finding different ways to explore data under the assumption that sometimes just looking at something from a different point of view reveals something novel … Being able to touch statistics can make them more engaging and bring instant understanding.”

It was bound to happen—two of the most towering concepts in tech coalscing into a single technomorphic constellation. 3D-printed social media. Does Hemsley even recognize what he has done? Perhaps not. But here’s how he did it. He used R, the free software programming language:

“First, I used the iGraph package to make a network from a edge list, which is simply a two column dataframe that represent the folks retweeting (list 1) and those they retweeted (list 2),” he writes. It only gets more technical from there. 

The resultant 3D network had too many thin connections—too many one-time tweet exchanges, too many solitary percolations of interest, too many unrequited retweets. 

“The network shown above is a bit too complex for today’s 3D printers; the gossamer thin links won’t hold up the nodes,” Hemsley explains. Which means the approximate social reality of the online networks will lose some fidelity in translation to matter; we will subtract or deform some friendships and bonds as our webs are moved off of the web and into real plastic life again.

3D printing is the easy part. Hemsley registered a Shapeways account, plunked down a Jackson, and waited for his skeletal model of social groupings to arrive in the mail. It looks like a sea urchin that got TP’d. And who’s that, way off in the margin on the right, alone?

The real question that remains now is whether our visual recognition software is ready to import the nodes of this network and transport those tenuous spatial relationships back onto the screen, where they belong.