Functional Dismantling of Network Relaxation through Slow-Branch Susceptibility

TL;DR

This paper introduces a spectral framework for analyzing and dismantling relaxation dynamics in asymmetric networks, focusing on the slow collective modes and their susceptibility to node removal.

Contribution

It develops a modal susceptibility score based on the Laplacian's nonzero spectral branch, providing a new method for assessing network robustness beyond traditional centrality measures.

Findings

MS identifies vulnerability patterns different from standard centrality-based attacks.

The framework unifies directed and undirected networks within a spectral-response formulation.

Tests show MS effectively predicts network fragility in synthetic and real-world networks.

Abstract

Robustness of relaxation on asymmetric networks is not determined by connectivity alone, because the slow collective mode can be complex and may change its spectral identity under adaptive damage. We introduce a slow-branch susceptibility framework for functional dismantling of network relaxation. Starting from the projected relaxation dynamics, we show that the relevant robustness observable is the real part of the selected nonzero Laplacian branch, which controls the long-time decay of the nonstationary sector. Node deletion is then treated as a dimension-changing compression of the operator, leading to a modal susceptibility (MS) score that estimates the first-order reduction of the branch-tracked relaxation rate from the biorthogonal support of the slow mode. In the reciprocal limit, the same construction reduces to the weighted Fiedler sector, placing directed and weighted-undirected networks within a common spectral-response formulation. Tests on synthetic and real-world networks show that MS identifies vulnerability patterns that differ from standard centrality-based attacks and edge-level spectral proxies. These results resolve a modal-selection ambiguity in non-Hermitian robustness analysis and provide a spectral basis for functional dismantling in asymmetric networks.