Abstract
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 by using an efficient EM algorithm, which distributes over the users, and has a linear time complexity. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our method models the real data better than other alternatives.
Highlights
The advances in location-acquisition techniques and the proliferation of mobile devices have generated an enormous amount of spatial and temporal data of users activities [1]
A considerable amount of spatio-temporal data is generated by the activity of users in location-based social networks (LBSN)
We propose a probabilistic generative model for the check-ins of users in location-based social networks, which can be used in predicting the future check-ins of the users, and discovering the latent influence network
Summary
The advances in location-acquisition techniques and the proliferation of mobile devices have generated an enormous amount of spatial and temporal data of users activities [1]. A considerable amount of spatio-temporal data is generated by the activity of users in location-based social networks (LBSN). In a typical LBSN, like Foursquare, users share the time and geolocation of their check-ins, comment about a venue, or unlock badges by exploring new venues. These data motivated the researchers to study the human spatio-temporal behavior in social networks [3, 4]. Given the history of users’ check-ins, the goal is to predict the time and location of users’ check-ins utilizing a model
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