机器学习拆解和复杂系统解体预警信号
Machine learning dismantling and early-warning signals of disintegration in complex systems
摘要：
从物理学到工程学，生物学和社会科学，自然和人工系统的特征是相互连接的拓扑结构，其特征-例如，异构连接，中尺度组织，层次结构-影响他们的鲁棒性，以外部扰动，如有针对性的攻击，他们的单位。识别最小攻击单元集以分解复杂网络，即网络拆除，是一个具有计算挑战性的（NP-难）问题，通常使用启发式算法进行攻击。在这里，我们证明了一台经过训练的机器能够识别更高阶的拓扑模式，从而比基于人类的机器更有效地分解大规模的社会、基础设施和技术网络。值得注意的是，该机器评估了下一次攻击将瓦解系统的可能性，提供了一种量化系统风险和检测系统崩溃预警信号的定量方法。这表明，机器辅助分析可以有效地用于政策和决策制定，以更好地量化复杂系统的脆弱性及其对冲击的反应。
英文摘要：
From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features - e.g., heterogeneous connectivity, mesoscale organization, hierarchy - affect their robustness to external perturbations, such as targeted attacks to their units. Identifying the minimal set of units to attack to disintegrate a complex network, i.e. network dismantling, is a computationally challenging (NP-hard) problem which is usually attacked with heuristics. Here, we show that a machine trained to dismantle relatively small systems is able to identify higher-order topological patterns, allowing to disintegrate large-scale social, infrastructural and technological networks more efficiently than human-based heuristics. Remarkably, the machine assesses the probability that next attacks will disintegrate the system, providing a quantitative method to quantify systemic risk and detect early-warning signals of system's collapse. This demonstrates that machine-assisted analysis can be effectively used for policy and decision making to better quantify the fragility of complex systems and their response to shocks.
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