Modeling and simulation of Japanese inter-firm network

Ozaki, Jun'ichi; Tamura, Koutarou; Takayasu, Hideki; Takayasu, Misako

doi:10.1007/s10015-018-0508-0

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…As already described, we use the money transport model based on the gravity interaction model 18 , 19 , 33 for the sales estimation. The money flow defines the link direction, where a firm i buying (outflow) a product from a supplying firm j (inflow) is represented by the link , and the network representation as adjacency matrix is set to .…”

Section: Methodsmentioning

confidence: 99%

“…The money flow defines the link direction, where a firm i buying (outflow) a product from a supplying firm j (inflow) is represented by the link , and the network representation as adjacency matrix is set to . The original gravity interaction model 18 , 19 , 33 approximates that the total money flows out of a firm is proportional to the firm sales to the power of . It also indicates that the quota of the money flows to each trading partner is proportional to the partner’s sales to the power of .…”

Section: Methodsmentioning

confidence: 99%

“…Instead of geography, we are much more preoccupied with the effects of mergers and acquisitions that, together with mortality, have a very significant impact of both trade and competition networks 14 , 15 , and it is not a focus within the referenced study. In addition, our model is based on the principle that the sales growth is a function of the structural changes in the network in line with established research 7 , 8 , 16 – 19 within the field, which is distinct from the approach adopted by the research on Corporate Competition 12 .…”

Section: Introductionmentioning

confidence: 99%

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Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

Ozaki,

Viegas,

Takayasu

et al. 2024

Sci Rep

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We develop a single two-layered model framework that captures and replicates both the statistical properties of the network as well as those of the intrinsic quantities of the agents. Our model framework consists of two distinct yet connected elements that were previously only studied in isolation, namely methods related to temporal network structures and those associated with money transport flows. Within this context, the network structure emerges from the first layer and its topological structure is transferred to the second layer associated with the money transactions. In this manner, we can explain how the micro-level dynamics of the agents within the network lead to the exogenous manifestation of the aggregated system statistical data en-wrapping the very same agents within the system. This is done by capturing the essential dynamics of collective motion in complex networks that enable the simultaneous emergence of tent-shaped distributions in growth rates within the agents, together with the emergence of scaling properties within the network in the study. We can validate the model framework and dynamics by applying these to the context of the real-world inter-firm trading network of firms in Japan and comparing the results of the statistical distributions at both network and agent levels in a temporal manner. In particular, we compare our results to the fundamental quantities supporting the seven empirical laws observed in data: the degree distribution, the mean degree growth rate over time, the age distribution of the firms, the preferential attachment, the sales distribution in steady states, their growth rates, their scaling relations generated by the model. We find these results to be nearly identical to the real-world data. The framework has the potential to be transformed into a forecasting tool to support decision-makers on financial and prudential policies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

Ozaki,

Viegas,

Takayasu

et al. 2024

Sci Rep

Self Cite

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show abstract

“…As already described, we use the money transport model based on the gravity interaction model 19,24,25 for the sales estimation. The money flow defines the link direction, where a firm i buying (outflow) a product from a supplying firm j (inflow) is represented by the link i → j, and the network representation as adjacency matrix is set to A i j = 1.…”

Section: Model For Network Money Transport Layer (B)mentioning

confidence: 99%

“…The money flow defines the link direction, where a firm i buying (outflow) a product from a supplying firm j (inflow) is represented by the link i → j, and the network representation as adjacency matrix is set to A i j = 1. The original gravity interaction model 19,24,25 approximates that the total money flows out of a firm is proportional to the firm sales to the power of α ∼ 0.9. It also indicates that the quota of the money flows to each trading partner is proportional to the partner's sales to the power of β ∼ 0.3.…”

Section: Model For Network Money Transport Layer (B)mentioning

confidence: 99%

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

Ozaki,

Viegas,

Takayasu

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

We develop a single two-layered model framework that captures and replicates both the statistical properties of the network as well as those of the intrinsic quantities of the agents. Our model framework consistently unifies two distinct yet interconnected elements that were previously only studied in isolation, namely methods related to temporal network structures and those associated with money transport flows. In this manner, we can explain how the micro-level dynamics of the agents within the network lead to the exogenous manifestation of the aggregated system statistical data en-wrapping the very same agents within the system. This is done by capturing the essential dynamics of collective motion in complex networks that enable the simultaneous emergence of tent-shaped distributions in growth rates within the agents, together with the emergence of scaling properties within the network in the study. We can validate the model framework and dynamics by applying these to the context of the real-world inter-firm trading network of firms in Japan and comparing the results of the statistical distributions at both network and agent levels in a temporal manner. In particular, we compare our results to the fundamental quantities supporting the seven empirical laws observed in data: the degree distribution, the mean degree growth rate over time, the age distribution of the firms, the preferential attachment, the sales distribution in steady states, their growth rates, their scaling relations generated by the model. We find these results to be nearly identical to the real-world data. The framework has the potential to be transformed into a forecasting tool to support decision-makers on financial and prudential policies.

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Reconstructing Networks

Cimini

Mastrandrea

Squartini

2021

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Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.

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Modeling and simulation of Japanese inter-firm network

Cited by 5 publications

References 11 publications

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

Reconstructing Networks

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