Abstract
Complexity theory has been used to study a wide range of systems in biology and nature but also business and socio-technical systems, e.g., see [1]. The ultimate objective is to develop the capability of steering a complex system towards a desired outcome. Recent developments in network controllability [2] concerning the reworking of the problem of finding minimal control configurations allow the use of the polynomial time Hopcroft- Karp algorithm instead of exponential time solutions. Subsequent approaches build on this result to determine the precise control nodes, or drivers, in each minimal control configuration [3], [4]. A browser-based analytical tool, CCTool1, for identifying such drivers automatically in a complex network has been developed in [5]. One key characteristic of a complex system is that it continuously evolves, e.g., due to dynamic changes in the roles, states and behaviours of the entities involved. This means that in addition to determining driver nodes it is appropriate to consider an evolving topology of the underlying complex network, and investigate the effect of removing nodes (and edges) on the corresponding minimal control configurations. The work presented here focuses on arriving at a classification of the nodes based on the effect their removal has on controllability of the network.