SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez nos Conditions d’utilisation et notre Politique de confidentialité.
SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez notre Politique de confidentialité et nos Conditions d’utilisation pour en savoir plus.
place graphs are the new social graphs Matt Biddulph @mattb | email@example.comEvery data scientist has their own favourite way of representing their data. For some peopleit’s Excel, and they think in rows and columns. For others it’s matrices, and they use linearalgreba to interrogate their data. For me, it’s graphs.
We’re all pretty used to the idea that you can model human relationships in a social graph.
“Social network analysis views social relationships in terms of network theory consisting of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors.”There’s a pretty deep area of mathematical study called Social Network Analysis that goesback at least 20 years. It tries to create insight by analysing the structure of social networks,and usually doesn’t incorporate any elements of culture or sociology in doing so.
Centrality measuresIt led to the creation of techniques like centrality measures, that try to ﬁnd the nodes that aremost central to the network. These might be the kind of people on Twitter who have thehighest chance of being retweeted.
Community detectionThere are also community detection algorithms that try to ﬁnd the most tightly-knitsubgraphs and cluster those nodes together. If you ran this over the network of people Ifollow on Twitter, it might be able to pick out my work colleagues or the people I socialisewith face-to-face.
People you may knowSites like LinkedIn build almost-telepathic “people you may know” features by walking aroundthe graph starting at your node and looking for people that show up a lot in yourneighbourhood that you haven’t connected with yet.
But enough mathematics. Let’s talk about Belgium.
Belgium is a country in the northwest of Europe with some unusual cultural qualities. It’ssandwiched between the Netherlands and France. About half of the country speaks French,and the other half speaks Dutch. It’d be very interesting to study the patterns of interactionsin this country.
Researchers at Louvain in Belgium were lucky enough to do a joint project with a Belgianmobile phone company. They had access to anonymised records of 2.6 million phone calls -the record of which phone called which number when.http://arxiv.org/pdf/0802.2178v2
Belgian phonecall networkFast unfolding of communities in large networks, Blondel et al They used these calls to construct a “call graph”. They were able to develop a community-detection algorithm that could detect the two separate clusters of Dutch and French speakersthat were mostly only calling each other. The algorithm achieved this simply by analysing theshape of the graph. It knew nothing about French, Dutch or phone calls.http://arxiv.org/pdf/0803.0476
So let’s take a step back and think about what other kinds of graph we could form, from whatkinds of data.
I work in location apps at Nokia, and so I naturally think of places. Wouldn’t it be interestingto study the connections between cities instead of people? For example, people probably ﬂymore often between NYC and LA than they do between NYC and New Jersey. We could re-draw the map based on closeness in the travel network.
I turned to the Hadoop cluster at Nokia and took a sample of several weeks of logs from ourrouting servers. These are used every time someone uses our maps application to request adriving route from one place to another. Every time someone drove from A to B, I made anedge in a “place graph” from A to B.
I ran the data through Gephi and asked it to cluster it based on the strength of connectionsbetween towns. The result is a not-quite-geographic new map of the world, where two citiesare close to each other if people often drive between them.
UK China Korea, Japan, etc Spain Most of Europe India Pakistan Finland RussiaAs you’d expect, the UK is an island and so people don’t drive in and out of it very often.Spain and Portugal are not islands, but they appear separate because they’re attached to therest of Europe by a very narrow neck of land. So people are much more likely to ﬂy than driveout of Spain.
How could we use this data in a practical application? Say I’m coming to New York to attend aconference on big data. I could choose a hotel near the conference venue, but I’d rather seemore interesting parts of New York.
Where should I stay?If I’ve never been to New York before, I could ask a friend. I could tell them that I likeLondon’s West End and San Francisco’s downtown.
Times Square = Piccadilly Circus New York LondonIf they know both towns, they’d probably tell me that Times Square is the Piccadilly Circus ofNew York.
What is the Greenwich Village of Tokyo? ... the Noe Valley of New York? ... the Shibuya of Los Angeles?But if we delve into the place graph, we could answer much more interesting questions, andcreate a “neighbourhood isomorphism” from city to city. People who like the Mission in SFand Shoreditch in London could ﬁnd out that Williamsberg is probably the best place forthem to stay in New York.