Inferring propagation paths for sparsely observed perturbations on complex networks

Abstract

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.

References in corpus (6)

• Prediction and predictability of global epidemics: the role of the airline transportation network
• Efficient Immunization Strategies for Computer Networks and Populations
• Missing and spurious interactions and the reconstruction of complex networks
• Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions
• A Poissonian explanation for heavy-tails in e-mail communication
• Systemic delay propagation in the US airport network