Evidence map›Paper›PMID 38351063›Full record

ArticleScientific reports2024

Commodity-specific triads in the Dutch inter-industry production network.

Marzio Di Vece, Frank P Pijpers, Diego Garlaschelli

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Marzio Di VeceIMT School for Advanced Studies Lucca, P.zza San Francesco 19, 55100, Lucca, Italy. marzio.divece@imtlucca.it.
Frank P PijpersStatistics Netherlands, Henri Faasdreef 312, 2492 JP, Den Haag, The Netherlands.
Diego GarlaschelliIMT School for Advanced Studies Lucca, P.zza San Francesco 19, 55100, Lucca, Italy.

Funding

SoBigData.it IR0000013
6 · The paper itself

Abstract

Triadic motifs are the smallest building blocks of higher-order interactions in complex networks and can be detected as over-occurrences with respect to null models with only pair-wise interactions. Recently, the motif structure of production networks has attracted attention in light of its possible role in the propagation of economic shocks. However, its characterization at the level of individual commodities is still poorly understood. Here we analyze both binary and weighted triadic motifs in the Dutch inter-industry production network disaggregated at the level of 187 commodity groups, which Statistics Netherlands reconstructed from National Accounts registers, surveys and known empirical data. We introduce appropriate null models that filter out node heterogeneity and the strong effects of link reciprocity and find that, while the aggregate network that overlays all products is characterized by a multitude of triadic motifs, most single-product layers feature no significant motif, and roughly 85% of the layers feature only two motifs or less. This result paves the way for identifying a simple 'triadic fingerprint' of each commodity and for reconstructing most product-specific networks from partial information in a pairwise fashion by controlling for their reciprocity structure. We discuss how these results can help statistical bureaus identify fine-grained information in structural analyses of interest for policymakers.

Identifiers

PMID38351063
PMCPMC10864404

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.