Inmaps

From LinkedIn’s networking graphing service; see also my map

I’ve been digging around in graph-mining and visualization tools lately, and this use at LinkedIn is one of the few cases where such things actually break through into mainstream usefulness. Well, perhaps not useful, but it’s nice to see how groups overlap.

In my chart here, the big tight-knit, self-referential cluster on the left is Joost, the TV startup I joined in 2006/7. At the top there is another tightly-linked community: the W3C team, where I worked 1999-2005. In between is a fuzzier cluster that I can only label ‘Web 2′, ‘Social Web’, … lots of Web technology standards sort of people. Then there are the linkers, like Max Froumentin and Robin Berjon between the W3C and Joost worlds, or Libby Miller and folk from the Asemantics and Apache scene (Alberto Reggiori, Stefano Mazzocchi) who link Joost through to the Semantic Web scene in the lower right.

The LinkedIn analysis finds distinct clusters that are fairly easy to identify as “Digital Libraries (Museums, Archives…)” and “Linked Data / RDF / Semantic Web”, even while being richly interconnected. I’m not suprised there’s a cluster for the VU University Amsterdam (even though well-linked to SW and digital libraries). However the presence of a BBC cluster was a surprise; either it shows how closely-knit the BBC community is, or just how much I’ve been hanging around with them. And that’s the intriguing thing; each individual map is just a per-person view, a thin slice through the bigger picture. It must be fun to see the whole dataset…

For more on all this, see LinkedIn or the inmaps site.

Loosly joined

find . -name danbri-\*.rdf -exec rapper –count {} \;


rapper: Parsing file ./facebook/danbri-fb.rdf
rapper: Parsing returned 2155 statements
rapper: Parsing file ./orkut/danbri-orkut.rdf
rapper: Parsing returned 848 statements
rapper: Parsing file ./dopplr/danbri-dopplr.rdf
rapper: Parsing returned 346 statements
rapper: Parsing file ./tribe.net/danbri-tribe.rdf
rapper: Parsing returned 71 statements
rapper: Parsing file ./my.opera.com/danbri-opera.rdf
rapper: Parsing returned 123 statements
rapper: Parsing file ./advogato/danbri-advogato.rdf
rapper: Parsing returned 18 statements
rapper: Parsing file ./livejournal/danbri-livejournal.rdf
rapper: Parsing returned 139 statements

I can run little queries against various descriptions of me and my friends, extracted from places in the Web where we hang out.

Since we’re not yet in the shiny OpenID future, I’m matching people only on name (and setting up the myfb: etc prefixes to point to the relevant RDF files). I should probably take more care around xml:lang, to make sure things match. But this was just a rough test…


SELECT DISTINCT ?n
FROM myfb:
FROM myorkut:
FROM dopplr:
WHERE {
GRAPH myfb: {[ a :Person; :name ?n; :depiction ?img ]}
GRAPH myorkut: {[ a :Person; :name ?n; :mbox_sha1sum ?hash ]}
GRAPH dopplr: {[ a :Person; :name ?n; :img ?i2]}
}

…finds 12 names in common across Facebook, Orkut and Dopplr networks. Between Facebook and Orkut, 46 names. Facebook and Dopplr: 34. Dopplr and Orkut: 17 in common. Haven’t tried the others yet, nor generated RDF for IM and Flickr, which I probably have used more than any of these sites. The Facebook data was exported using the app I described recently; the Orkut data was done via the CSV format dumps they expose (non-mechanisable since they use a CAPCHA), while the Dopplr list was generated with a few lines of Ruby and their draft API: I list as foaf:knows pairs of people who reciprocally share their travel plans. Tribe.net, LiveJournal, my.opera.com and Advogato expose RDF/FOAF directly. Re Orkut, I noticed that they now have the option to flood your GTalk Jabber/XMPP account roster with everyone you know on Orkut. Not sure the wisdom of actually doing so (but I’ll try it), but it is worth noting that this quietly bridges a large ‘social network ing’ site with an open standards-based toolset.

For the record, the names common to my Dopplr, Facebook and Orkut accounts were: Liz Turner, Tom Heath, Rohit Khare, Edd Dumbill, Robin Berjon, Libby Miller, Brian Kelly, Matt Biddulph, Danny Ayers, Jeff Barr, Dave Beckett, Mark Baker. If I keep adding to the query for each other site, presumably the only person in common across all accounts will be …. me.