Multi-periodic Contact Patterns in Predicting Future Contacts over Mobile Networks

With advances in Internet technology and prominence of mobile and smart devices in our lives, opportunistic and pervasive networks are now ubiquitous in solving many existing service limitations. The challenge lies in the underlying time-varying graph of the network due to mobility and intermittent connectivity. This introduces technical limitations in successful realization of services and applications e.g., efficient routing, maximal coverage with minimal latency, data offloading, and effective dissemination over mobile networks. Efficient solution to these inter-related problems lies in the novel prediction strategies for most accurate future contacts (i.e., links or interactions). In contrast to the existing strategies that consider either network structure or regular pattern and periodic nature of contacts, we propose novel use of seasonal autoregressive integrated moving average model and recurrent neural network model that are capable of capturing multi-periodic, dependent contact patterns. We predict the number of contacts relative to a node and over all nodes in any future interval over a given user and a pair of users. Finally, we validate our models with three distinct empirical data set, and compare with doubly recurrent and homogeneous Poisson process model to demonstrate the superiority of our prediction models.