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International Conference on Network Analysis

NET 2018: Network Algorithms, Data Mining, and Applications pp 143–160Cite as

Indirect Influence Assessment in the Context of Retail Food Network

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 315))

Abstract

We consider an application of long-range interaction centrality (LRIC) to the problem of the influence assessment in the global retail food network. Firstly, we reconstruct an initial graph into the graph of directed intensities based on individual node’s characteristics and possibility of the group influence. Secondly, we apply different models of the indirect influence estimation based on simple paths and random walks. This approach can help us to estimate node-to-node influence in networks. Finally, we aggregate node-to-node influence into the influence index. The model is applied to the food trade network based on the World International Trade Solution database. The results obtained for the global trade by different product commodities are compared with classical centrality measures.

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Acknowledgements

The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ’5–100’.

The work related to the analysis of global retail food network (Sect. 4) was prepared within the framework of the Russian Science Foundation under grant No 17-18-01651.

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Authors and Affiliations

  1. National Research University Higher School of Economics, 20 Myasnitskaya Str., Moscow, 101000, Russian Federation

    Fuad Aleskerov, Natalia Meshcheryakova & Sergey Shvydun

  2. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Str., Moscow, 117997, Russian Federation

    Fuad Aleskerov, Natalia Meshcheryakova & Sergey Shvydun

Authors
  1. Fuad Aleskerov
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  2. Natalia Meshcheryakova
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  3. Sergey Shvydun
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Corresponding author

Correspondence to Natalia Meshcheryakova .

Editor information

Editors and Affiliations

  1. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Ilya Bychkov

  2. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Valery A. Kalyagin

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  4. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Oleg Prokopyev

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Aleskerov, F., Meshcheryakova, N., Shvydun, S. (2020). Indirect Influence Assessment in the Context of Retail Food Network. In: Bychkov, I., Kalyagin, V., Pardalos, P., Prokopyev, O. (eds) Network Algorithms, Data Mining, and Applications. NET 2018. Springer Proceedings in Mathematics & Statistics, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-030-37157-9_10

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