Resilience-Metric Assortativity and Structural Fragility in Complex Policy-Relevant Networks

• In Event: Evaluating the Resilience of Defense-critical Supply and Demand Networks

Abstract

Understanding how systemic fragility emerges in interconnected social, economic, and infrastructural systems is a central challenge for public policy. Traditional network analyses often focus on degree distributions, clustering, or centrality, but these measures alone cannot capture how vulnerability propagates through heterogeneous systems. In this work we introduce a new structural indicator, resilience‑metric assortativity, that quantifies the extent to which nodes with similar resilience characteristics cluster together within a network. Positive assortativity indicates that highly resilient entities tend to connect to one another, potentially forming stabilizing substructures, while negative assortativity suggests that resilient and fragile nodes are interwoven in ways that may accelerate systemic risk. We develop this metric within a generalizable framework grounded in complex systems science and apply it to large-scale, policy‑relevant networks. Using weighted adjacency matrices derived from empirical data, we compute resilience‑metric assortativity across hundreds of potential subnetworks and evaluate how structural patterns correlate with observed fragility, failure cascades, and recovery dynamics. The method integrates node‑level resilience scores, which are derived from dynamical stability analysis, with network topology, enabling a richer understanding of how local robustness interacts with global structure. From a policy perspective, this metric provides a new diagnostic tool for identifying structural weaknesses that are not visible through conventional analysis. It  potentially offers actionable insights for designing interventions that enhance systemic resilience; such as targeted reinforcement of bridging nodes, restructuring of fragile subnetworks, or strategic diversification of connections. The approach is computationally efficient, interpretable, and adaptable to a wide range of public policy applications, including disaster preparedness, supply chain stability, financial regulation, and public administration networks. By integrating nonlinear dynamics with network science, this work advances the methodological toolkit available to policy researchers and practitioners. It demonstrates how resilience‑aware structural analysis can inform more robust governance strategies in an era of increasing complexity and interdependence.

Author

• Jeremie A Fish, Clarkson University
  Presenting Author