  Social networks are getting closer to our real physical world. People share
the exact location and time of their check-ins and are influenced by their
friends. Modeling the spatio-temporal behavior of users in social networks is
of great importance for predicting the future behavior of users, controlling
the users' movements, and finding the latent influence network. It is observed
that users have periodic patterns in their movements. Also, they are influenced
by the locations that their close friends recently visited. Leveraging these
two observations, we propose a probabilistic model based on a doubly stochastic
point process with a periodic decaying kernel for the time of check-ins and a
time-varying multinomial distribution for the location of check-ins of users in
the location-based social networks. We learn the model parameters using an
efficient EM algorithm, which distributes over the users. Experiments on
synthetic and real data gathered from Foursquare show that the proposed
inference algorithm learns the parameters efficiently and our model outperforms
the other alternatives in the prediction of time and location of check-ins.
