Assembling real networks from synthetic and unstructured subsets: the corporate reporting case
File(s)
Published version
Author(s)
Viegas, Eduardo Marcello
Goto, Hayato
Takayasu, Hideki
Takayasu, Misako
Jensen, Henrik Jeldoft
Type
Journal Article
Abstract
The analysis of interfirm business transaction networks provides invaluable insight into the trading dynamics and economic
structure of countries. However, there is a general scarcity of data available recording real, accurate and extensive information
for these types of networks. As a result, and in common with other types of network studies - such as protein interactions for
instance - research tends to rely on partial and incomplete datasets, i.e. subsets, with less certain conclusions. Hereh, we
make use of unstructured financial and corporate reporting data in Japan as the base source to construct a financial reporting
network, which is then compared and contrasted to the wider real business transaction network. The comparative analysis
between these two rich datasets - the proxy, partially derived network and the real, complete network at macro as well as local
structural levels - provides an enhanced understanding of the non trivial relationships between partial sampled subsets and
fully formed networks. Furthermore, we present an elemental agent based pruning algorithm that reconciles and preserves key
structural differences between these two networks, which may serve as an embryonic generic framework of potentially wider
use to network research, enabling enhanced extrapolation of conclusions from partial data or subsets.
structure of countries. However, there is a general scarcity of data available recording real, accurate and extensive information
for these types of networks. As a result, and in common with other types of network studies - such as protein interactions for
instance - research tends to rely on partial and incomplete datasets, i.e. subsets, with less certain conclusions. Hereh, we
make use of unstructured financial and corporate reporting data in Japan as the base source to construct a financial reporting
network, which is then compared and contrasted to the wider real business transaction network. The comparative analysis
between these two rich datasets - the proxy, partially derived network and the real, complete network at macro as well as local
structural levels - provides an enhanced understanding of the non trivial relationships between partial sampled subsets and
fully formed networks. Furthermore, we present an elemental agent based pruning algorithm that reconciles and preserves key
structural differences between these two networks, which may serve as an embryonic generic framework of potentially wider
use to network research, enabling enhanced extrapolation of conclusions from partial data or subsets.
Date Issued
2019-07-30
Date Acceptance
2019-07-17
Citation
Scientific Reports, 2019, 9
ISSN
2045-2322
Publisher
Nature Research
Journal / Book Title
Scientific Reports
Volume
9
Copyright Statement
© The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
MODEL
0601 Biochemistry and Cell Biology
0299 Other Physical Sciences
Publication Status
Published
Article Number
ARTN 11075