Target-Aware Network Dismantling with Limited Budgets

2026-10-06

AI Summary

This study addresses the inefficiency of conventional contact-tracing quarantine strategies under resource constraints. It reveals the counterintuitive phenomenon that isolating network-central nodes outperforms contact tracing when intervention budgets are scarce. Employing generating function analysis, deep reinforcement learning (RAIL), and network topology modeling, this work identifies budget-dependent critical transition points for intervention strategies. Building upon these findings, it proposes an adaptive local-risk intervention framework coupled with a dynamic switching mechanism. By challenging traditional epidemiological paradigms, the proposed approach consistently maximizes population-level protection and effectively suppresses epidemic propagation across diverse synthetic and real-world networks.

Abstract

During epidemics, limited resources dictate critical decisions about whom to isolate or monitor. Conventional public health strategies intuitively prioritize known infected individuals and their immediate contacts. Here, we reveal that this contact-centric approach is fundamentally suboptimal under realistic resource constraints. By formulating this challenge as target-aware network dismantling, we uncover a counter-intuitive, budget-dependent intervention transition. Generating-function analysis demonstrates that when intervention budgets are scarce, isolating structurally central individuals, rather than the immediate neighbours of the infected, protects a vastly larger population. The conventional neighbour-targeting strategy only becomes optimal as available resources increase. Because this critical transition point shifts dynamically with the intervention budget, network topology, and spatial distribution of infections, static strategies inevitably fail. To overcome this, we develop Risk-Aware Isolation Learning (RAIL), a deep reinforcement learning framework that adaptively selects optimal interventions based on the evolving residual network. Across diverse synthetic and real-world networks, RAIL consistently maximizes population protection and suppresses outbreak spread. Ultimately, our findings challenge prevailing epidemiological intuitions, establishing a generalizable framework for adaptive interventions against localized risks in public health and other complex systems.

Problem

• Network Dismantling
• Epidemic Control
• Limited Budget
• Target-Aware Intervention
• Resource Allocation

Innovation

• Target-Aware Network Dismantling
• Deep Reinforcement Learning
• Generating-Function Analysis
• Risk-Aware Isolation Learning (RAIL)
• Budget-Dependent Transition