Authors:
Andrey V. Parinov,Nikolay N. Tolstyh,Yuri K. Yazov,Vladimir I. Belonozhkin,Olga A. Ostapenko,DOI NO:
https://doi.org/10.26782/jmcms.spl.8/2020.04.00006Keywords:
Social network,epidemic,content,probability of infection,representative sample of data,Abstract
This paper considers the varieties and peculiarities of content perception in social networks. It analyzes the data about communication topology and the probability of user’s infection. The methodology of the representative sampling is suggested. The authors focused on discrete simulation of the epidemic process. In this connection, the topological models were used as a triadic predicate (vertex-arc-vertex) describing the incidence and communication strength of social network users. In this context, the matrices have been built: of the vertices degree, the weighted centrality of the network elements, and the specific balance of the content volume. These matrices characterize the topological properties of the weighted (taking into account the traffic of its arcs and vertices) of the network from which then the sampling takes place. This is due to the need to reduce the size of the network being analyzed and therefore its representative truncation is carried out, i.e. the conversion of the original data to a form suitable for later simulation of epidemics. The paper introduces a fairly detailed review of the variety of content circulating in social networks. For its intended purpose, content is divided into entertainment, useful, news, user, reputation, interactive, and commercial. Special attention is paid to destructions in content as well as the ways to draw attention to it. All of this constitutes an information base for modeling the diffusion processes of content in social networks. The above matrices serve this purpose. In addition, the paper introduces the results of the proposed methodology use in application to the development of information support required for the modeling of social networks. In this context, an example of a three-dimensional illustration of the source network and its representative sample by level of specific traffic is given. The issue of the mutation of the distribution law of verteces degrees during representative sample was discussed. In the discussion of the results obtained in the paper, the directions of further improvement of the methodology has been formulated which could be used as a basis for other researchers. First of all they are the following: structural and parametric details of social networks descriptions, comprehensive research of the content constructs and ways of its promotion in the network, taking into account the change dynamics of analyzed network parameters, and users participation in several information communities at the same time. This would greatly enrich the information support for social networks modeling.Refference:
I. Find the Most Shared Content and Key Influencers. – Электрон. дан. – Режимдоступа: http://buzzsumo.com.
II. Большая инструкция для начинающих SMM специалистов Точка доступа: http://www.terehoff.com/the-best-smm-manual.html
III. https://habrahabr.ru/post/286662/.
IV. Neil Patel. How Long Should Each Blog Post Be? A Data Driven Answer –https://www.quicksprout.com/2014/03/31/how-long- should-each- blog-post- be-a- data-driven- answer/.
V. KONECT (the Koblenz Network Collection) Social Network statistics and analysis. –http://konect.uni-koblenz.de
VI. https://ru-ru.facebook.com.
VII. https://vk.com.
VIII. http://androidconnect.ru/statistika-facebook- kolichestvo-aktivnyh- polzovatelej-naschityvaetsya- okolo-1- 7-milliarda-ezhemesyachno- 2.
IX. https://plus.google.com.
X. https://soundcloud.com.
XI. https://www.youtube.com.
XII. https://www.flickr.com/
XIII. http://ratengoods.com.
XIV. https://ru.foursquare.com.
XV. http://tourout.ru.
XVI. http://www.wikireality.ru/wiki/4pda.
XVII. http://forum.xda-developers.com.
XVIII. https://www.reddit.com.
XIX. https://www.blogger.com.
XX. https://www.tumblr.com.
XXI. http://www.livejournal.com.
XXII. http://digg.com
XXIII. https://slashdot.org.
XXIV. http://www.bibsonomy.org.
XXV. http://www.advogato.org.
XXVI. http://www.last.fm/ru.
XXVII. http://www.ligo.org.
XXVIII. Ostapenko, G.A. Analytical estimation of the component viability of distribution automated information datasystem / G.A. Ostapenko, D.G. Plotnicov, O.Y Makarov, N.M. Tikhomirov, V.G. Yurasov // World AppliedSciences Journal. – 2013. – 25 (3). – P. 416-420.
XXIX. Ostapenko, G.A. Analytical models of information-psychological impact of social information networks on users /G.A. Ostapenko, L.V. Parinova, V.I. Belonozhkin, I.L. Bataronov, K.V. Simonov // World Applied SciencesJournal. – 2013. – 25 (3). – P. 410-415.
XXX. Ermakov, S.A. Optimization of expert methods used to analyze information security risk in modern wirelessnetworks / S.A. Ermakov, A.S. Zavorykin, N.S. Kolenbet, A.G. Ostapenko, A.O Kalashnikov // Life ScienceJournal. – 2014. – № 11(10s). – P. 511-514.
XXXI. Radko, N.M. Assessment of the system's EPI-resistance under conditions of information epidemic expansion /N.M. Radko, A.G. Ostapenko, S.V. Mashin, O.A. Ostapenko, D.V. Gusev // Biosciences Biotechnology ResearchAsia. – 2014. – Vol. 11 (3). – P. 1781-1784.
XXXII. Radko, N.M. Peak risk assessing the process of information epidemics expansion / N.M. Radko, A.G. OstapenkoS.V. Mashin, O.A. Ostapenko, A.S. Avdeev // Biosciences Biotechnology Research Asia. – 2014. – Vol. 11(Spl.End). – P. 251-255.
XXXIII. Ostapenko, A.G. Flood-attacks within the hypertext information transfer protocol: damage assessment and
XXXIV. management / A.G. Ostapenko, M.V. Bursa, G.A. Ostapenko, D.O. Butrik // Biosciences Biotechnology ResearchAsia. – 2014. – Vol. 11 (Spl.End). – P. 173-176.
XXXV. Islamgulova, V.V. Discreet risk-models of the process of the development of virus epidemics in non-uniformnetworks / V.V. Islamgulova, A.G. Ostapenko,,N.M. Radko, R.K. Babadzhanov, O.A. Ostapenko // Journal ofTheoretical and Applied Information Technology. – 2016. – P. 306-315.