Estimation of Length of Node-to-Node Paths Distribution in the Global Network
https://doi.org/10.18255/1818-1015-2020-1-6-21
Abstract
The experiment aimed at finding a distribution of path lengths between nodes in the global network and an estimation of parameters of that distribution is described.In particular, the method of measurement of path length with traceroute utility of the GNU/Linux system and limitations on the selection of nodes imposed by traceroute are described. e measurement results are provided and high values of skewness and kurtosis for all resulting distributions are noted. Simulation model of this experiment was developed to test the experiment validity in the determination of distribution parameters in the global network. This model is also described. It is shown that high values of skewness and kurtosis of the measured distributions are not the result of the measurement technique, therefore the global network could not be described by the Barabasi–Albert ´ model. Several most viable hypotheses explaining diffierences in skewness and kurtosis of experimentally obtained pathlength distribution estimations and values derived from the Barabasi–Albert model are listed. Results of diffierent hypotheses ´ simulations are provided. It is shown that the most fitting hypothesis is that definitive influence on skewness and kurtosis of path-length distribution estimations is caused by the quasi pre-fractal structure of the global network.
About the Authors
Alexandra I. Kononova
National Research University of Electronic Technology
Russian Federation
PhD.
Russian Federation
PhD.
Alexey V. Gorodilov
Russkaya Moda (Russian fashion)
Russian Federation
PhD.
Russian Federation
PhD.
References
1. A. Gorodilov, A. Kononova, and V. Shangin, “Osobennosti peredachi dannyh v decentralizovannyh piringovyh setyah”, Proceedings of Universities. Electronics, vol. 98, no. 6, pp. 95–97, 2012.
2. A. P. Shiryaev, A. V. Dorofeev, A. R. Fedorov, L. G. Gagarina, and V. V. Zaycev, “LDA models for finding trends in technical knowledge domain”, in 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), IEEE, 2017, pp. 551–554.
3. D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010.
4. A. Fronczak, P. Fronczak, and J. A. Hołyst, “Average path length in random networks”, Physical Review E, vol. 70, no. 5, p. 056 110, 2004.
5. R. Sedgewick, Algorithms in C, Part 5: Graph Algorithms, 2002.
6. H. Jeong, B. Tombor, Z. N. Albert R.and Oltvai, and A.-L. Barabasi, “The large-scale organization of ´ metabolic networks”, Nature, vol. 407, no. 6804, pp. 651–654, 2000.
7. R. Albert and A.-L. Barabasi, “Statistical mechanics of complex networks”, ´ Reviews of modern physics, vol. 74, no. 1, pp. 47–97, 2002.
8. A. Gorodilov A.V.and Kononova, “Simulation as tool of error assessment of experimental measurement of path-lengths distribution in global network”, Sistemy komp’yuternoj matematiki i ih prilozheniya: materialy XX Mezhdunarodnoj nauchnoj konferencii, vol. 1, pp. 34–41, 2019.
9. R. A. Kochkarov, D. A. Pavlov, and D. A.-Z. Hubieva, “Fractal and preefactal graphs, basic definitions and symbols”, Scientific journal of KubSAU (Polythematic online scientific journal of Kuban State Agrarian University), no. 134, pp. 174–188, 2017.
10.
Review
For citations:
Kononova A.I., Gorodilov A.V. Estimation of Length of Node-to-Node Paths Distribution in the Global Network. Modeling and Analysis of Information Systems. 2020;27(1):6-21. (In Russ.) https://doi.org/10.18255/1818-1015-2020-1-6-21
Views:
666
Email this article 