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Stochastic Processes with Trend Stationarity in High-Clustered Growth Networks

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Intelligent Sustainable Systems (ICoISS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 665))

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Abstract

Many social and media networks add new users and their connections as they evolve. One of the realistic models describing the growth of complex networks that takes into account the peculiarities of the local node clustering is the triadic closure model. In this paper, we study the stochastic process that represents the behavior of the average degree of all neighbors for a node in a network generated by the triadic closure model. It is known that the expected values of this local characteristic grow according to a power law. We show that the process is trend-stationary, and moreover, the dynamics of the variation coefficient of the average degree of the node’s neighbors tends to zero over time. We discuss the result applicability to the analysis of media and social networks.

Supported by the Russian Science Foundation, project 22-18-00153.

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Authors and Affiliations

  1. Saratov State University, Saratov, 410012, Russia

    Sergei Sidorov, Sergei Mironov & Sophia Tikhonova

Authors
  1. Sergei Sidorov
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  2. Sergei Mironov
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  3. Sophia Tikhonova
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Corresponding author

Correspondence to Sergei Sidorov .

Editor information

Editors and Affiliations

  1. Gnanamani College of Engineering and Technology, Namakkal, Tamil Nadu, India

    Jennifer S. Raj

  2. Department of Computer Engineering and Informatics, University of Patras, Kato Kastritsi, Greece

    Isidoros Perikos

  3. Aurel Vlaicu, University of Arad, Arad, Romania

    Valentina Emilia Balas

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sidorov, S., Mironov, S., Tikhonova, S. (2023). Stochastic Processes with Trend Stationarity in High-Clustered Growth Networks. In: Raj, J.S., Perikos, I., Balas, V.E. (eds) Intelligent Sustainable Systems. ICoISS 2023. Lecture Notes in Networks and Systems, vol 665. Springer, Singapore. https://doi.org/10.1007/978-981-99-1726-6_21

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