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Recurrent spatio-temporal modeling of check-ins in location-based social networks

Zarezade, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1371/journal.pone.0197683
  3. Publisher: Public Library of Science , 2018
  4. Abstract:
  5. 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. © 2018 Zarezade et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
  6. Keywords:
  7. Aarticle ; Friend ; Human ; Social network ; Stochastic model
  8. Source: PLOS ONE ; Volume 13, Issue 5 , 23 May , 2018 ; 19326203 (ISSN)
  9. URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197683