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Computational Complexity of SRIC and LRIC Indices

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Network Algorithms, Data Mining, and Applications (NET 2018)

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Abstract

Over the past years, there is a deep interest in the analysis of different communities and complex networks. Identification of the most important elements in such networks is one of the main areas of research. However, the heterogeneity of real networks makes the problem both important and problematic. The application of SRIC and LRIC indices can be used to solve the problem since they take into account the individual properties of nodes, the possibility of their group influence, and topological structure of the whole network. However, the computational complexity of such indices needs further consideration. Our main focus is on the performance of SRIC and LRIC indices. We propose several modes on how to decrease the computational complexity of these indices. The runtime comparison of the sequential and parallel computation of the proposed models is also given.

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Acknowledgements

The paper was prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ‘5-100’. This work is also supported by the Russian Foundation for Basic Research under grant -18-01-00804a Power of countries in the food security problem.

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Authors and Affiliations

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

    Sergey Shvydun

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

    Sergey Shvydun

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  1. Sergey Shvydun
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Correspondence to Sergey Shvydun .

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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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Shvydun, S. (2020). Computational Complexity of SRIC and LRIC Indices. 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_4

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