Percolation on feature-enriched interconnected systems

TL;DR

This paper introduces a new mathematical framework for percolation on feature-enriched interconnected systems, allowing for more realistic robustness assessments by considering node importance based on diverse features.

Contribution

It presents a novel framework that generalizes percolation to networks with enriched features and importance-based node removal, addressing complex real-world scenarios.

Findings

Framework effectively models robustness with feature-based node removal

Enables analysis of networks with diverse feature types

Improves realism of percolation studies in interconnected systems

Abstract

Percolation is an emblematic model to assess the robustness of interconnected systems when some of their components are corrupted. It is usually investigated in simple scenarios, such as the removal of the system's units in random order, or sequentially ordered by specific topological descriptors. However, in the vast majority of empirical applications, it is required to dismantle the network following more sophisticated protocols, for instance, by combining topological properties and non-topological node metadata. We propose a novel mathematical framework to fill this gap: networks are enriched with features and their nodes are removed according to the importance in the feature space. We consider features of different nature, from ones related to the network construction to ones related to dynamical processes such as epidemic spreading. Our framework not only provides a natural generalization of percolation but, more importantly, offers an accurate way to test the robustness of networks in realistic scenarios.