  In an ego-network, an individual (ego) organizes its friends (alters) in
different groups (social circles). This social network can be efficiently
analyzed after learning representations of the ego and its alters in a
low-dimensional, real vector space. These representations are then easily
exploited via statistical models for tasks such as social circle detection and
prediction. Recent advances in language modeling via deep learning have
inspired new methods for learning network representations. These methods can
capture the global structure of networks. In this paper, we evolve these
techniques to also encode the local structure of neighborhoods. Therefore, our
local representations capture network features that are hidden in the global
representation of large networks. We show that the task of social circle
prediction benefits from a combination of global and local features generated
by our technique.
