Detecting Hidden Layers from Spreading Dynamics on Complex Networks

TL;DR

This paper introduces methods to detect and reconstruct hidden layers in multilayer networks based on spreading dynamics, improving reliability testing and unobserved path identification in complex systems.

Contribution

It presents a novel likelihood-based approach for hidden layer detection in multilayer networks using spreading cascade data, outperforming null models.

Findings

High success rates in hidden layer detection on synthetic networks

Effective reconstruction of hidden layers in real-world networks

Method outperforms baseline null models

Abstract

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the Susceptible-Infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. We conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.