Morphological Inversion of Complex Diffusion

TL;DR

This paper introduces a new morphological operator to identify the initial source of a diffusion process in complex networks, outperforming traditional methods across various systems.

Contribution

The paper presents a novel morphological inversion technique that is model-agnostic and effective for tracing the origin of diffusion phenomena in diverse networked systems.

Findings

Outperforms randomized and centrality-based methods

Applicable to various diffusion systems

Effective in identifying initial sources in complex networks

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.