  The location check-ins of users through various location-based services such
as Foursquare, Twitter, and Facebook Places, etc., generate large traces of
geo-tagged events. These event-traces often manifest in hidden (possibly
overlapping) communities of users with similar interests. Inferring these
implicit communities is crucial for forming user profiles for improvements in
recommendation and prediction tasks. Given only time-stamped geo-tagged traces
of users, can we find out these implicit communities, and characteristics of
the underlying influence network? Can we use this network to improve the next
location prediction task? In this paper, we focus on the problem of community
detection as well as capturing the underlying diffusion process and propose a
model COLAB based on Spatio-temporal point processes in continuous time but
discrete space of locations that simultaneously models the implicit communities
of users based on their check-in activities, without making use of their social
network connections. COLAB captures the semantic features of the location,
user-to-user influence along with spatial and temporal preferences of users. To
learn the latent community of users and model parameters, we propose an
algorithm based on stochastic variational inference. To the best of our
knowledge, this is the first attempt at jointly modeling the diffusion process
with activity-driven implicit communities. We demonstrate COLAB achieves up to
27% improvements in location prediction task over recent deep point-process
based methods on geo-tagged event traces collected from Foursquare check-ins.
