Morphological Inversion of Complex Diffusion

Abstract

Epidemics, neural cascades, power failures, and many other phenomena can be described by a diffusion process on a network. To identify the causal origins of a spread, it is often necessary to identify the triggering initial node. Here we define a new morphological operator and use it to detect the origin of a diffusive front, given the final state of a complex network. Our method performs better than randomized trials as well as centrality based methods. More importantly, our method is applicable regardless of the specifics of the forward model, and therefore can be applied to a wide range of systems such as identifying the patient zero in an epidemic, pinpointing the neuron that triggers a cascade, identifying the original malfunction that causes a catastrophic infrastructure failure, and inferring the ancestral species from which a heterogeneous population evolves.

13 pages, 8 figures

References in corpus (5)

• Power grids vulnerability: a complex network approach
• Inferring the origin of an epidemic with a dynamic message-passing algorithm
• Bayesian inference of epidemics on networks via Belief Propagation
• Identification of Patient Zero in Static and Temporal Networks - Robustness and Limitations
• The zero-patient problem with noisy observations

Cited by in corpus (2)

• Metastability and dynamic modes in magnetic island chains
• Limits on Inferring the Past