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

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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Publication Details

Journal
PLoS ONE
Published
2025-09-03
DOI
https://doi.org/10.1371/journal.pone.0330372
Citations
1
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.32
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article

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

Jamshid Ardalankia, Ali Habibnia, Marcel Ausloos, Gholamreza Jafari
1 citations
PLoS ONE
Complex Network Analysis Techniques
1.32
article

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

Jamshid Ardalankia, Ali Habibnia, Marcel Ausloos, Gholamreza Jafari
article en
1 citations

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.

PLoS ONEVol. 20(9)
London Metropolitan University (GB), University of Leicester (GB), Statistics Belgium (BE), Babeș-Bolyai University (RO), Shahid Beheshti University (IR), Virginia Tech (US), Punjab Engineering College (IN), Bucharest University of Economic Studies (RO)
Openalex Percentile: Top 16%
Complex Network Analysis Techniques
1.32
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