Projecting Social Networks in Dynamic Environments for Tracking Purposes

Tracking individuals in social networks is one of the challenging tasks in digital forensics. In social networks, individuals' behavior can be predicted or measured based on their online interactions. Moreover, the behavior of individuals in real life can be a side effect of their online interactions. Therefore, it is important to investigate this issue in terms of the interactions among people. To this end, this paper suggests a novel framework for projecting a social network into a dynamic environment aiming to simulate a real-life situation. A dataset of users' interactions from Facebook is used in the simulations. The users along with their attributes are projected into a dynamic simulation environment. Then, a mobility model and a population distribution are incorporated into the simulation environment. The interactions among users in the dynamic environment are collected and compared to the interactions in the social network. The findings showed that the behavior of users was inherited from the social network. Based on these findings, the proposed approach reflected efficient performance and a strong ability to project a static network into a dynamic one. The idea of this work is applicable in rural applications for monitoring and tracking purposes.