Flow approaches to community allocation in complex network systems: Fìz.-mat. model. ìnf. tehnol. 2021, 33:122-127 - Polishchuk, Olexandr (PHYSICO-MATHEMATICAL MODELLING AND INFORMATIONAL TECHNOLOGIES, 2021)
The paper investigates the problem of finding communities in complex network systems, the detec-tion of which allows a better understanding of the laws of their functioning. To solve this problem, two approaches are proposed based on the use of flows characteristics of complex network. The first of these approaches consists in calculating the parameters of influence of separate subsys-tems of the network system, distinguished by the principles of ordering or subordination, and the second, in using the concept of its flow core. Based on the proposed approaches, reliable criteria for finding communities have been formulated and efficient algorithms for their detection in com-plex network systems have been developed. It is shown that the proposed approaches make it pos-sible to single out communities in cases in which the existing numerical and visual methods turn out to be disabled. References Newman, M. E. J. (2012). Communities, modules and large-scale structure in networks. Nature Physics, 8, 25–31. Newman, M. E. J. (2004). Detecting community structure in networks. European Phy¬sical Journal, 38(2), 321–330. Khan, B. S., Niazi, M. A. (2017). Network community detection: A Review and Visual Survey. arXiv: 1708.00977 [cs.SI]. Kolomeichenko, M. I., M. Kolomeichenko, I. V. Polyakov, Chepovsky, A. A., Chepovsky, A. M. (2016). Selection of communities in the graph of interacting objects. Fundamental and Applied Mathematics, 21(3), 131-139. Girvan, M., Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the USA, 99, 7821-7826. DOI https://doi.org/10.1073/pnas.122653799 Blondel, V. D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E. (2008). The Louvain method for community detection in large networks. Journal of Statistical Mechanics. Theory and Experi¬ments, 108-121. Radicchi, C., Castellano, C., Cecconi, F., Loreto, V., Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the USA, 101(9), 2658-2663. DOI https://doi.org/10.1073/pnas.0400054101 Donetti, L., Munoz, M. A. (2005). Improved spectral algorithm for the detection of network communities arXiv: physics/0504059 [physics.soc-ph]. Rosvall M., Bergstrom, C. T. (2007). An information-theoretic framework for resolving community structure in complex networks. Proceedings of the National Academy of Sciences of the USA, 104(18), 7327-7331. DOI https://doi.org/10.1073/pnas.0611034104 Lambiotte R., Rosvall, M. (2012). Ranking and clustering of nodes in networks with smart teleportation. Physical Review E, 85(5). DOI https://doi.org/10.1103/physreve.85.056107 Babak, F., Naghmeh, M. (2015). Growing multiplex networks with arbitrary number of layers arXiv: 1506.06278v2 [physics.soc-ph]. Kolomeichenko, M. I., Chepovskiy, A. M. (2014). Visualization and analysis of large graphs. Business Informatics, 30(4), 7-16. Polishchuk, O. D., Yadzhak, M. S. (2018). Network structures and systems: І. Flow characteristics of complex networks. System information and information technologies, 2, 42-54. DOI https://doi.org/10.20535/srit.2308-8893.2018.2.05 Polishchuk, O. D., Yadzhak, M. S. (2018). Network structures and systems: II. Core networks and multiplexes. System pre-sludge and information technologies, 3, 38-51. DOI https://doi.org/10.20535/srit.2308-8893.2018.3.04 Polishchuk, O. D., Yadzhak, M. S. (2018). Network structures and systems: III. Hierarchies and networks. Systems research and information technologies, 4, 82-95. DOI https://doi.org/10.20535/srit.2308-8893.2018.4.07