Evidence map›Paper›PMID 40901868›Full record

ArticlePloS one2025

Hidden community interlayer spillover detection in financial multilayer networks: Generalization of hierarchical clustering to multilayer networks.

Jamshid Ardalankia, Ali Habibnia, Marcel Ausloos, G Reza Jafari

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Article in PloS one, 2025. 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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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jamshid ArdalankiaDepartment of Economics, Virginia Tech, Blacksburg, Virginia, United States of America.
Ali HabibniaDepartment of Economics, Virginia Tech, Blacksburg, Virginia, United States of America.
Marcel AusloosSchool of Business, University of Leicester, Brookfield, Leicester, United Kingdom.ORCID https://orcid.org/0000-0001-9973-0019
G Reza JafariDepartment of Physics, Shahid Beheshti University, Evin, Tehran, Iran.ORCID https://orcid.org/0000-0002-0448-5549

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interdependent networks structurally influence each other so that the source network imposes hidden community structures into the target network. We propose a mathematical model so that when introducing an interlayer similarity function we generalize hierarchical clustering approaches for multilayer networks. The proposed methodology shows how a "source" network influences the "target" network via structural spillovers that are hidden and are not detectable by conventional community detection methods. The methodology reveals evidence that hidden interlayer interactions consequently generate hidden links on the target network. These hidden links construct hidden community structures on the target network (imposed from the source network) that are distinct from the community structures of the solo target network (without the presence of the source network). This model applies to systems with hidden interlayer interactions, such as, e.g., covert criminal groups, inter-platform social network interactions, scientific research groups, and financial markets. Financial markets are well known for complicated endogenous and exogenous, but often hidden, not to say the least, asymmetric layer interactions. We implement our model on multilayer financial networks: in particular, we find that trading value logarithmic changes (source) impose hidden community structures on the price return network (target). The main finding is that adding another relevant layer, such as the trading value layer, adds more information to systemic behaviors throughout the price return network. Dismissing it may yield less systemic information and underestimation of systemic risk because the footprint of some structures on the target network originated from another layer and is not detectable by singling out the target layer. As an empirical application, we exploit the methodology to define another perspective on portfolio diversification.

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Models, EconomicAlgorithmsCluster AnalysisHumansModels, Theoretical

Identifiers

PMID40901868
PMCPMC12407434

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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.