Detailed-level modelling of influence spreading on complex networks

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2024-12

Department

Department of Computer Science

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Language

en

Pages

21

Series

Scientific Reports, Volume 14, issue 1, pp. 1-21

Abstract

The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.

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Publisher Copyright: © The Author(s) 2024.

Keywords

Centrality measure, Community detection, Complex network, Computational social science, Network spreading model, Social network

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DOI

10.1038/s41598-024-79182-9

Citation

Kuikka, V & Kaski, K K 2024, 'Detailed-level modelling of influence spreading on complex networks', Scientific Reports, vol. 14, no. 1, 28069, pp. 1-21. https://doi.org/10.1038/s41598-024-79182-9

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https://urn.fi/URN:NBN:fi:aalto-202411297521

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[article-cris] Perustieteiden korkeakoulu / SCI