Dynamic range maximization in excitable networks

TL;DR

This paper presents an efficient algorithm to maximize the dynamic range of excitable networks by strategically removing the minimal number of links to reach criticality, balancing sensitivity and connectivity.

Contribution

It introduces a novel eigenvalue-based method for identifying optimal link removals to achieve criticality in excitable networks.

Findings

The proposed algorithm effectively maximizes dynamic range with minimal link removal.

It outperforms other heuristics in synthetic and real-world networks.

The method maintains network connectivity while optimizing dynamic range.

Abstract

We study the strategy to optimally maximize the dynamic range of excitable networks by removing the minimal number of links. A network of excitable elements can distinguish a broad range of stimulus intensities and has its dynamic range maximized at criticality. In this study, we formulate the activation propagation in excitable networks as a message passing process in which the critical state is reached when the largest eigenvalue of the weighted non-backtracking (WNB) matrix is exactly one. By considering the impact of single link removal on the largest eigenvalue, we develop an efficient algorithm that aims to identify the optimal set of links whose removal will drive the system to the critical state. Comparisons with other competing heuristics on both synthetic and real-world networks indicate that the proposed method can maximize the dynamic range by removing the smallest number of links, and at the same time maintain the largest size of the giant connected component.