COUPLED HIDDEN MARKOV MODELS FOR USER ACTIVITY IN SOCIAL NETWORKS

We consider the problem of developing data-driven probabilistic models describing the activity profile of users in online social network settings. Previous models of user activities have discarded the potential influence of a user's network structure on his temporal activity patterns. Here we address this shortcoming and suggest an alternative approach based on coupled Hidden Markov Models (HMM), where each user is modeled as a hidden Markov chain, and the coupling between different chains is allowed to account for social influence. We validate the model using a significant corpus of user activity traces on Twitter, and demonstrate that the coupled HMM explains and predicts the observed activity profile more accurately than a renewal process-based model or a conventional uncoupled HMM, provided that the observations are sufficiently long to ensure accurate model learning.