Observer Selection for Source Identification on Complex Networks

We consider the problem of selecting an optimal observer set in order to maximize the probability of identifying the source of diffusion on complex networks with deterministic propagation delay. A maximum likelihood (ML) estimator is proposed to choose the most informative nodes for observation and a greedy algorithm is used to implement the estimation efficiently. Numerical simulations show the effectiveness of our algorithm.