Deriving production chains using restricted gradient extraction

So as to assess systemic economic value and risk, it is of crucial interest to develop methods to detect interwoven production chains in large-scale economies. Commodity transactions between firms induce a complex network in which it is challenging to identify production chains, not only due to the size of the underlying network but also because of its inherent cyclical connections and loops. We present a novel method, Restricted Gradient Extraction (RGE), which is based on Hodge decomposition, which is capable of extracting the gradient flow of a production network. The RGE method, being of relatively low computational complexity, is demonstrated to both a synthetic and real country-sized production network. Application of RGE on the syntactic data set shows that the resulting gradient flow is a directed acyclic graph, a weighted subgraph of the original network, and the gradient flow is retained. Application to the economic production network of the Netherlands shows that production chains can be readily detected and described. The method is applicable to weighted directed networks in general and is not limited to economic production networks.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-05-01
DOI
https://doi.org/10.1063/5.0270180
Primary Topic
Computational Drug Discovery Methods
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article
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article

Deriving production chains using restricted gradient extraction

Michel Mandjes, Edwin de Jonge, F. P. Pijpers
Chaos An Interdisciplinary Journal of Nonlinear Science
Computational Drug Discovery Methods
article

Deriving production chains using restricted gradient extraction

Michel Mandjes, Edwin de Jonge, F. P. Pijpers
article en

Abstract

So as to assess systemic economic value and risk, it is of crucial interest to develop methods to detect interwoven production chains in large-scale economies. Commodity transactions between firms induce a complex network in which it is challenging to identify production chains, not only due to the size of the underlying network but also because of its inherent cyclical connections and loops. We present a novel method, Restricted Gradient Extraction (RGE), which is based on Hodge decomposition, which is capable of extracting the gradient flow of a production network. The RGE method, being of relatively low computational complexity, is demonstrated to both a synthetic and real country-sized production network. Application of RGE on the syntactic data set shows that the resulting gradient flow is a directed acyclic graph, a weighted subgraph of the original network, and the gradient flow is retained. Application to the economic production network of the Netherlands shows that production chains can be readily detected and described. The method is applicable to weighted directed networks in general and is not limited to economic production networks.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(5)
Leiden University (NL), Centraal Bureau voor de Statistiek (NL), Mathematical Institute of the Slovak Academy of Sciences (SK), University of Amsterdam (NL)
Decent work and economic growth
Openalex Percentile: Top 87%
Computational Drug Discovery Methods
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Deriving production chains using restricted gradient extraction — Michel Mandjes, Edwin de Jonge, et al. · Chaos An Interdisciplinary Journal of Nonlinear Science (2025) | TGRS Research Map | TGRS