Fixed Parameter Algorithms and Hardness of Approximation Results for the Structural Target Controllability Problem

Recent research has revealed new and unexpected applications of network control science within biomedicine, pharmacology, and medical therapeutics. These new insights and new applications generated in turn a rediscovery of some old, unresolved algorithmic problems. One of these problems is the Structural Target Control optimization problem, known in previous literature also as Structural Output Controllability problem, which is defined as follows. Given a directed network and a target subset of nodes, the task is to select a small (or the smallest) set of nodes from which the target can be independently controlled, i.e., there exists a set of paths from the selected set of nodes (called driver nodes) to the target nodes such that no two paths intersect at the same distance from their targets. Recently, Structural Target Control optimization problem has been shown to be NP-hard, and several heuristic algorithms were introduced and analyzed, both on randomly generated networks, and on biomedical ones. In this paper, we show that the Structural Target Controllability problem is fixed parameter tractable when parameterized by the number of target nodes. We also prove that the problem is hard to approximate at a factor better than O(log n). Taking into consideration the real case formulations of this problem we identify two more parameters which are naturally constrained by smaller bounds: the maximal length of a controlling path and the size of the set of nodes from which the control can start. With these new parameters we provide an approximation algorithm which is of exponential complexity in the size of the set of nodes from which the control can start and polynomial in all the other parameters.