Many complex systems can be described by agents that can be modeled as a network of dynamically interacting cyclo-stationary processes. Such systems arise in areas like power systems and climate sciences. For many of these systems a key objective is to understand mutual influences between various subsystems without altering the natural behavior of the system. Such an objective translates to unveiling the interconnection of the topology of the network using only passive means. Most existing related works have emphasized correlation based methods where interdependencies over different time-instants can be missed. Recent work where dynamic influences are incorporated assuming stationary statistics cannot accommodate applications that arise in many areas such as power and climate sciences. In this article an algorithm based on Wiener filtering is devised for the reconstruction of interconnectivity of dynamically related cyclo-stationary processes. It is shown that all existing interdependencies are detected and spurious detection remains local. Application to a microgrid power network is shown to yield useful insights.