LinkedIn, the “professional” career-oriented social network, is one of those places on the Web where I maintain a profile for visibility. I’m yet to gain any practical value whatsoever from it. That said, I know plenty of people who do find it useful – mostly, it seems, those living near the north-east or west coast of the USA.
LinkedIn have something of a reputation for innovation – see LinkedIn Labs, their small demonstration products, for example. The latest of these is named InMaps. It’s been popping up on blogs and Twitter for several days. Essentially, it creates a graph of your LinkedIn network, applies some community detection algorithm to cluster the members and displays the results as a pretty, interactive graphic that you can share.
What seems to have captured the imagination is that the graphs indicate communities that are instantly recognisable to the user. There’s mine on the right (click for full-size version). It’s not a large, complex or especially interesting network but when I “eyeballed” it, I was immediately able to classify the three sub-graphs:
- Orange – mostly people with whom I have worked or currently work, plus a few “random” contacts: note that this group is hardly interconnected at all
- Green – people who work in bioinformatics or computational biology, particularly genomics: two major hubs connect me with this group
- Blue – the largest, densest network is composed largely of what I’d call the “BioGang”: people that I interact with on Twitter and FriendFeed, many of whom I haven’t met in person
This confirms what I’ve long suspected: I prefer to network with smart strangers than my immediate peers and colleagues. Or as Bill Joy said, “no matter who you are, most of the smartest people work for someone else.” I’ve seen this misquoted as “where you are”, which makes more sense to me.