Guiding propagation to localized target nodes in complex networks

Spreading is an important type of dynamics in complex networks that can be used to model numerous real processes such as epidemic contagion and information propagation. In the literature, there are many methods in vital node identification and node immunization proposed for controlling the spreading processes. As a novel research problem, target spreading aims to minimize or maximize propagation toward a group of target nodes. In this paper, we consider a situation where the initial spreader emerges randomly in the network and one has to guide the propagation toward localized targets in the network. To this end, we propose a guided propagation and a reversed guided propagation model, which adaptively guides the spreading process by allocating the limited number of recovery nodes in each spreading step. We study in detail the impact of infection rate and recovery rate on the model. Simulation results show the validity of our models in most cases. Finally, we find that this adaptive target spreading can be achieved under situations with multiple groups of target nodes.