Exploring the landscape of community-based dismantling strategies

TL;DR

This paper introduces a community-based network dismantling framework that identifies robust strategies for node removal, effective across different algorithms, with practical implications for cost-efficient network disruption.

Contribution

It proposes a general, community-aware dismantling method that remains effective regardless of the community detection algorithm used, highlighting robustness and operational advantages.

Findings

Dismantling strategies are robust across different community detection algorithms.

Multiple effective strategies can be identified with minimal node removal.

The percolation threshold is consistent across various real-world networks.

Abstract

Network dismantling is a relevant research area in network science, gathering attention both from a theoretical and an operational point of view. Here, we propose a general framework for dismantling that prioritizes the removal of nodes that bridge together different network communities. The strategies we detect are not unique, as they depend on the specific realization of the community detection algorithm considered. However, when applying the methodology to some real-world networks we find that the percolation threshold at which dismantling occurs is strongly robust, and it does not depend on the specific algorithm. Thus, the stochasticity inherently present in many community detection algorithms allows to identify several strategies that have comparable effectiveness but require the removal of distinct subsets of nodes. This feature is highly relevant in operational contexts in which the removal of nodes is costly and allows to identify the least expensive strategy that still holds high effectiveness.