32. Detecting Influential Nodes in Complex Networks with Range Probabilistic Control Centrality

Dynamic complex networks illustrate how ‘agents’ interact by exchanging information, in a network that is constantly changing; an example of such networks is a vehicular ad hoc network. This article investigates the issue of influence propagation in dynamic, complex networks, and in particular, it proposes a method for identifying influential nodes in a network with probabilistic links. Based on control-theoretic concepts, we develop the range probabilistic control centrality (RPCC). For evaluation purposes, we used the susceptible, infected, recovered (SIR) model, which is simple model for epidemic spreading assuming no births or deaths, accepting that the incubation period of the infectious agent is instantaneous, and that the duration of infectivity is same as length of the disease; it also assumes a completely homogeneous population with no age, spatial, or social structure. Our experimentation shows that the proposed identification method is able to recognize very effective spreaders. The key feature of these nodes is that they are positioned at the beginning of ‘strong’ paths, upon which paths a large number of other nodes lies.