This study examines the complex dependency network within the global aerospace sector, focusing on publicly traded companies across the aerospace, airline, airport, and aircraft manufacturing industries. Unlike previous studies that predominantly employed linear correlation methods, this research uses distance correlation to capture both linear and non-linear dependencies, providing a more nuanced view of interdependency within the sector. Additionally, we apply the Planar Maximally Filtered Graph and the Leiden community detection algorithm to identify clusters of companies with similar market behaviors and strategic alignments. By analyzing the network before and after the COVID-19 pandemic, this study provides insights into the impact of significant global disruptions on industry rankings, network stability, and sector resilience. This work uniquely contributes to the existing literature by offering a quantitative, network-based framework that maps sector-wide dependencies and highlights the dynamic structural shifts in the aerospace industry in response to external shocks. The findings provide actionable insights for investors, policymakers, and stakeholders, aiding in informed decision-making, risk mitigation, and identifying strategic opportunities.