RT Journal Article
SR Electronic
T1 Inferring propagation paths for sparsely observed perturbations on complex networks
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP e1501638
DO 10.1126/sciadv.1501638
VO 2
IS 10
A1 Massucci, Francesco Alessandro
A1 Wheeler, Jonathan
A1 Beltrán-Debón, Raúl
A1 Joven, Jorge
A1 Sales-Pardo, Marta
A1 Guimerà, Roger
YR 2016
UL http://advances.sciencemag.org/content/2/10/e1501638.abstract
AB In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space” (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.