Find Me If You Can: Improving Geographical Prediction with Social and Spatial Proximity

http://cameronmarlow.com/media/backstrom-geographical-prediction_0.pdf

Geography and social relationships are inextricably inter-
twined; the people we interact with on a daily basis almost
always live near us. As people spend more time online,
data regarding these two dimensions { geography and so-
cial relationships { are becoming increasingly precise, allow-
ing us to build reliable models to describe their interaction.
These models have important implications in the design of
location-based services, security intrusion detection, and so-
cial media supporting local communities.
Using user-supplied address data and the network of asso-
ciations between members of the Facebook social network,
we can directly observe and measure the relationship be-
tween geography and friendship. Using these measurements,
we introduce an algorithm that predicts the location of an
individual from a sparse set of located users with perfor-
mance that exceeds IP-based geolocation. This algorithm
is e cient and scalable, and could be run on hundreds of
millions of users.