  People's personal social networks are big and cluttered, and currently there
is no good way to automatically organize them. Social networking sites allow
users to manually categorize their friends into social circles (e.g. 'circles'
on Google+, and 'lists' on Facebook and Twitter), however they are laborious to
construct and must be updated whenever a user's network grows. In this paper,
we study the novel task of automatically identifying users' social circles. We
pose this task as a multi-membership node clustering problem on a user's
ego-network, a network of connections between her friends. We develop a model
for detecting circles that combines network structure as well as user profile
information. For each circle we learn its members and the circle-specific user
profile similarity metric. Modeling node membership to multiple circles allows
us to detect overlapping as well as hierarchically nested circles. Experiments
show that our model accurately identifies circles on a diverse set of data from
Facebook, Google+, and Twitter, for all of which we obtain hand-labeled
ground-truth.
