Mapping analytical methods between input–output economics and network science

Abstract The input–output (IO) model can be used to examine the flow of products and services within an economy, resembling a network with industries as nodes and transactions as links. Diverging significantly from commonly studied networks such as social, protein, and power grids, IO networks exhibit intricate interconnectivity, involving weighted nodes and both directional and weighted links. This uniqueness necessitates careful consideration when applying complex network analysis techniques to IO systems. We critically review current complex network metrics and attempt to link them with existing IO approaches. Based on our assessment, certain network metrics, such as degree centrality and eigenvector centrality, have been explicitly integrated into the IO theory. In contrast, there exist metrics whose definitions and interpretations expand when applied in the context of IO analysis, including closeness and betweenness centrality. Additionally, network metrics are usually used to study topological features, identify key sectors, and construct novel metrics to study related issues. Network metrics used in IO analysis can identify important driver and transmission sectors in resource flow and environmental emission network, facilitating the development of targeted and reliable strategies. Besides, network metrics are used to quantify topological features and structural changes of the IO network which help strengthen the supply chain and mitigate both direct and indirect impacts of disruptions. Our ultimate goal is to establish connections and offer a roadmap for developing network‐based tools in IO analysis.