This document discusses using graphs to represent relationships between places instead of people. It describes how researchers analyzed anonymized phone call records from Belgium to construct a "call graph" and identify separate clusters of Dutch and French speakers. The author analyzed routing logs from a location app to create a "place graph" showing connections between cities based on how often people drive between them. This place graph could be used to recommend neighborhoods in an unfamiliar city based on similarities to neighborhoods a traveler likes in cities they know.

1. place graphs
are the new
social graphs
Matt Biddulph
@mattb | matt@hackdiary.com
Every data scientist has their own favourite way of representing their data. For some people
it’s Excel, and they think in rows and columns. For others it’s matrices, and they use linear
algreba to interrogate their data. For me, it’s graphs.

2. We’re all pretty used to the idea that you can model human relationships in a social graph.

3. “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 goes
back 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.

4. Centrality
measures
It led to the creation of techniques like centrality measures, that try to find the nodes that are
most central to the network. These might be the kind of people on Twitter who have the
highest chance of being retweeted.

5. Community
detection
There are also community detection algorithms that try to find the most tightly-knit
subgraphs and cluster those nodes together. If you ran this over the network of people I
follow on Twitter, it might be able to pick out my work colleagues or the people I socialise
with face-to-face.

6. People you
may know
Sites like LinkedIn build almost-telepathic “people you may know” features by walking around
the graph starting at your node and looking for people that show up a lot in your
neighbourhood that you haven’t connected with yet.

7. But enough mathematics. Let’s talk about Belgium.

8. Belgium is a country in the northwest of Europe with some unusual cultural qualities. It’s
sandwiched 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 interactions
in this country.

9. Researchers at Louvain in Belgium were lucky enough to do a joint project with a Belgian
mobile 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

10. Belgian
phonecall
network
Fast unfolding of communities in large networks, Blondel et al [2008]
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 speakers
that were mostly only calling each other. The algorithm achieved this simply by analysing the
shape of the graph. It knew nothing about French, Dutch or phone calls.
http://arxiv.org/pdf/0803.0476

11. So let’s take a step back and think about what other kinds of graph we could form, from what
kinds of data.

12. I work in location apps at Nokia, and so I naturally think of places. Wouldn’t it be interesting
to study the connections between cities instead of people? For example, people probably fly
more 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.

13. I turned to the Hadoop cluster at Nokia and took a sample of several weeks of logs from our
routing servers. These are used every time someone uses our maps application to request a
driving route from one place to another. Every time someone drove from A to B, I made an
edge in a “place graph” from A to B.

14. I ran the data through Gephi and asked it to cluster it based on the strength of connections
between towns. The result is a not-quite-geographic new map of the world, where two cities
are close to each other if people often drive between them.

15. UK
China
Korea,
Japan, etc
Spain Most of Europe
India
Pakistan
Finland Russia
As 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 the
rest of Europe by a very narrow neck of land. So people are much more likely to fly than drive
out of Spain.

16. How could we use this data in a practical application? Say I’m coming to New York to attend a
conference on big data. I could choose a hotel near the conference venue, but I’d rather see
more interesting parts of New York.

17. Where should
I stay?
If I’ve never been to New York before, I could ask a friend. I could tell them that I like
London’s West End and San Francisco’s downtown.

18. Times Square = Piccadilly Circus
New York London
If they know both towns, they’d probably tell me that Times Square is the Piccadilly Circus of
New York.

19. 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, and
create a “neighbourhood isomorphism” from city to city. People who like the Mission in SF
and Shoreditch in London could find out that Williamsberg is probably the best place for
them to stay in New York.

20. Thank you.
Matt Biddulph
@mattb | matt@hackdiary.com