Our digital society is becoming more and more integrated facilitated by advancements in communication technology and services connecting our society from financial transactions to social interactions. As a result, large quantities of data on our interactions, which can be used to quantitatively model and predict the evolution of social systems, are being generated daily. Mathematically, these data can be characterized in terms of large and multiplex networks of how interactions between entities evolve in time forming so-called dynamic networks. As the digitalization of our interactions are disrupting practically all aspects of society from the financial sector to how information is shared, it is important to understand these complex dynamical systems of interactions and be able to foresee their behaviors. This is the focus of this project: How can we develop efficient computational tools for the analyses of large dynamic complex networks that can i) enable a human understanding of the structure of these complex systems and ii) forecast their future behaviors?