CONTROLLABILITY ANALYSIS OF COMPLEX NETWORKS USING STATISTICAL RANDOM SAMPLING

Large complex dynamical systems behave in ways that reflect their structure. There are many applications where we need to control these systems by bringing them from their current state to a desired state. Affecting the state of these systems is done by communications with its key elements, called driver nodes, in reference to their representation as a network of nodes. Over the past decades, much focus has been paid on analytical approaches to derive optimal control configurations based on the concept of Minimum Driver node Sets (MDSs) for directed complex networks. However, the underlying control mechanisms of many complex systems rely on quickly controlling a major subspace of a system. In this work, we ask how complex networks behave if driver nodes are randomly selected? We seek to understand and employ the statistical characteristics of MDSs to randomly select driver nodes and analyze the controllability properties of complex network. We propose an algorithm to build Random Driver node Sets (RDSs) and analyze their controllable subspace, the minimum time needed to control, and the cardinality of RDSs. Through our evaluations on a variety of synthetic and real-world networks, we show RDSs can quickly and effectively control a major subspace of networks.