Peak Risk Assessing The Process of Information Epidemics Expansion

Radko, Nikolay Mikhaylovich; Ostapenko, Alexander Grigorievich; Mashin, Sergey Vyacheslavovich; Ostapenko, Olga Aleksandrovna; Avdeev, Artem Sergeyevich

doi:10.13005/bbra/1471

Cited by 3 publications

(7 citation statements)

References 2 publications

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“…The graph, obtained by means of the similar transformation, preserves the basic properties of the initial one; however, in contrast to it, it is convenient for conducting risk-analysis, determination of a probable damage (Ostapenko, Plotnicov, Makarov, Tikhomirov & Yurasov, 2013;Ostapenko, Parinova, Belonozhkin, Bataronov & Simonov, 2013;Ermakov, Zavorykin, Kolenbet, Ostapenko & Kalashnikov, 2014;Radko, Ostapenko, Mashin, Ostapenko & Gusev, 2014;Radko, Ostapenko, Mashin, Ostapenko & Avdeev, 2014;Ostapenko, Bursa, Ostapenko & Butrik, 2014;Islamgulova, Ostapenko, Radko, Babadzhanov & Ostapenko, 2016), and treatment by other algorithms that are of interest to a researcher (Evin & Habibullin, 2012;Ostapenko, Plotnikova & Guzev, 2016;Kuwata, 2009;Park, Moore & Bader, 2010;Zhao, Kumar, Harrison & Yen, 2011).…”

Section: Resultsmentioning

confidence: 99%

“…Such graph is convenient for calculation of different metrics and conducting riskanalysis (Ostapenko, Plotnicov, Makarov, Tikhomirov & Yurasov, 2013;Ostapenko, Parinova, Belonozhkin, Bataronov & Simonov, 2013;Ermakov, Zavorykin, Kolenbet, Ostapenko & Kalashnikov, 2014;Radko, Ostapenko, Mashin, Ostapenko & Gusev, 2014;Radko, Ostapenko, Mashin, Ostapenko & Avdeev, 2014;Ostapenko, Bursa, Ostapenko & Butrik, 2014;Islamgulova, Ostapenko, Radko, Babadzhanov & Ostapenko, 2016). In this case it is possible to operate with combined vertices (concentrators), which determine the overall behavior of the network, instead of specific vertices, which in majority are of small significance (in view of the demonstrative law of distribution of the degrees of vertices in social networks) and do not exert influence on content distribution.…”

Section: Discussionmentioning

confidence: 99%

“…Practical research of epidemiological processes in real social networks requires an automated toolkit of simulation (Ostapenko, Plotnicov, Makarov, Tikhomirov & Yurasov, 2013;Ostapenko, Parinova, Belonozhkin, Bataronov & Simonov, 2013;Ermakov, Zavorykin, Kolenbet, Ostapenko & Kalashnikov, 2014;Radko, Ostapenko, Mashin, Ostapenko & Gusev, 2014;Radko, Ostapenko, Mashin, Ostapenko & Avdeev, 2014;Ostapenko, Bursa, Ostapenko & Butrik, 2014;Islamgulova, Ostapenko, Radko, Babadzhanov & Ostapenko, 2016).Such a toolkit must implement all the functions that are necessary for modelling suspended heterogeneous social networks, and include the whole set of the harmful content known at the moment (Ostapenko, Plotnikova & Guzev, 2016). For all that, it is important to choose the type of model that describes the network under study to the fullest degree.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, mathematical apparatus of the graph theory is applied as a main research tool, where suspended graphs act as social networks (Ostapenko, Plotnicov, Makarov, Tikhomirov & Yurasov, 2013;Ostapenko, Parinova, Belonozhkin, Bataronov & Simonov, 2013;Ermakov, Zavorykin, Kolenbet, Ostapenko & Kalashnikov, 2014;Radko, Ostapenko, Mashin, Ostapenko & Gusev, 2014;Radko, Ostapenko, Mashin, Ostapenko & Avdeev, 2014;Ostapenko, Bursa, Ostapenko & Butrik, 2014;Islamgulova, Ostapenko, Radko, Babadzhanov & Ostapenko, 2016;Ostapenko, Plotnikova & Guzev, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there arises the necessity to build the algorithm of concentration of the content, isolation of vertices subjected to combination, and formation of the combined edges for the purpose of preserving basic bonds among the combined vertices (hubs). Subsequently, this will permit to apply existing mechanisms of risk-analysis and topological analysis of the networks to assessment of the transformed network (Ostapenko, Plotnicov, Makarov, Tikhomirov &Yurasov, 2013;Ostapenko, Parinova, Belonozhkin, Bataronov & Simonov, 2013;Ermakov, Zavorykin, Kolenbet, Ostapenko & Kalashnikov, 2014;Radko, Ostapenko, Mashin, Ostapenko & Gusev, 2014;Radko, Ostapenko, Mashin, Ostapenko & Avdeev, 2014;Ostapenko, Bursa, Ostapenko & Butrik, 2014;Islamgulova, Ostapenko, Radko, Babadzhanov & Ostapenko, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Risk analysis in the management of forest fire in Russia

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Morkovin

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et al. 2019

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In the Russians forests, from 10 to 15 thousand forest fires are annually registered, and according to forecasts, the tendency of increasing damage from fires in the country’s forests will grow. The final annual damage from forest fires in 2010 amounted to 550.4 thousand hectares of forest covered by fire. The article discusses the risks of forest fires determined by a combination of natural, economic and social factors. Based on an expert survey, an assessment was made of the probability of occurrence of risk events taking into account the categories of forests in Russia. The expert assessment used two criteria - the probability of a forest fire and the extent of expected losses from the occurrence of risk. It is shown that the most vulnerable to forest fires are forests located near homes, social and industrial facilities, as well as recreational forests. Three categories of forests have been proposed for the purpose of continuous monitoring of fire danger, firstly, forests located near dwellings, social and industrial facilities, secondly, forests with a developed network of roads and having a high level of timber value.

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The automated networks and regional users: risk analysis of their reactions to the attacks of different destructive orientation

Eshchenko

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Батаронов

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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In work manifestations of an information war in the context of attacks to the automated networks by means of malicious codes and destructive contents are considered. Special attention is paid to users of the social automated networks which are final subject to these attacks. In this regard, authors offer a series of analytical expressions, the considering capacities of a great number of users with a different reaction to destructive content. At sufficient community of the offered technique, the emphasis is placed on the regional aspect. In particular, it is offered by polling to investigate regional resources for the purpose of forecasting of dangerous actions of their users induced by the content of different destructive orientation. All this in total forms risk model which can form a methodical basis for decisionmaking on counteraction of the attacks of the automated networks by means of destructive content.

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Peak Risk Assessing The Process of Information Epidemics Expansion

Abstract: In this article, we propose a probabilistic development model of information epidemic and approaches to peak assessment of epi-resistance based on an analytical risk analysis of systems viral infection under a variety of information infections sources.

Cited by 3 publications

References 2 publications

Transformation of Network Models Considering Their Topological Properties and Weight Characteristics

Transformation of Network Models Considering Their Topological Properties and Weight Characteristics

Risk analysis in the management of forest fire in Russia

The automated networks and regional users: risk analysis of their reactions to the attacks of different destructive orientation

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