<p>The exploration of multiplex networks has become an emerging field of research due to the existence of different layers with diverse connectivity structures in almost every real network. Effectively managing a multiplex network is challenging, requiring the identification of vital spreaders (i.e., nodes) by measuring their importance. After reviewing the existing studies, it becomes clear that the dominance of individual layers has not been systematically evaluated in isolation in multiplex networks. In this study, we present a novel approach to measure the dominance of individual layers depending on two distinct parameters: node activeness and edge activeness. After that, we calculate the centrality value for each node on a per-layer basis and construct a centrality vector based on existing centrality methods and a novel Closeness-based Layer Gravity (CLG) method. Finally, the vital spreaders are identified by evaluating the importance of nodes through a mapping technique that aggregates the dominance of the layers with centrality values of the nodes from the respective layers. This proposed framework independently measures layer dominance and identifies vital spreaders, making it well suited for distributed and high-performance computing environments, and ensuring scalability across large multiplex networks. The performance of our proposed method is evaluated against the multiplex-based SIR epidemic simulator, and we observe that amalgamating our proposed layer dominance concept with the CLG method effectively identifies vital nodes, achieving a maximum average ranking similarity of <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7713_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="51" /> </InlineMediaObject> <EquationSource Format="TEX">$80.93\%$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>80.93</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> across various percentages of identified spreaders, considering eight real multiplex networks. By evaluating network robustness through the normalized LCC (<i>Largest Connected Component</i>) value after removing various percentages of identified spreaders, our method achieves the lowest average normalized LCC value of 0.751, outperforming state-of-the-art approaches.</p>

Identifying vital spreaders in multiplex networks: measurement of layer dominance and a closeness-based layer gravity method

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发表日期：

2025年8月19日

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Suman Nandi,
Giridhar Maji,
Animesh Dutta

摘要

The exploration of multiplex networks has become an emerging field of research due to the existence of different layers with diverse connectivity structures in almost every real network. Effectively managing a multiplex network is challenging, requiring the identification of vital spreaders (i.e., nodes) by measuring their importance. After reviewing the existing studies, it becomes clear that the dominance of individual layers has not been systematically evaluated in isolation in multiplex networks. In this study, we present a novel approach to measure the dominance of individual layers depending on two distinct parameters: node activeness and edge activeness. After that, we calculate the centrality value for each node on a per-layer basis and construct a centrality vector based on existing centrality methods and a novel Closeness-based Layer Gravity (CLG) method. Finally, the vital spreaders are identified by evaluating the importance of nodes through a mapping technique that aggregates the dominance of the layers with centrality values of the nodes from the respective layers. This proposed framework independently measures layer dominance and identifies vital spreaders, making it well suited for distributed and high-performance computing environments, and ensuring scalability across large multiplex networks. The performance of our proposed method is evaluated against the multiplex-based SIR epidemic simulator, and we observe that amalgamating our proposed layer dominance concept with the CLG method effectively identifies vital nodes, achieving a maximum average ranking similarity of $80.93\%$ $80.93 %$ across various percentages of identified spreaders, considering eight real multiplex networks. By evaluating network robustness through the normalized LCC (Largest Connected Component) value after removing various percentages of identified spreaders, our method achieves the lowest average normalized LCC value of 0.751, outperforming state-of-the-art approaches.