Volume 3(52)

CONTENT

Kalney A. M. Layered Network Models (Overview)
Migov D.A., Korotkov A.N. Cuts using for modeling the propagation of cascading failures in electrical power grids
Akishin V. A. Cognitive model for evaluating customer experience in the structure of infocommunication landscape of a telecom operator
S. V. Bredikhin, V. M. Lyapunov, N. G. Scherbakova The structure and parameters of the unweighted co-authorship network based on DB RePEc data
Mironov V.V. New scientific index of the author's publishing activity

Technograd Plus Ltd, 630087, Novosibirsk Russia,

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

Layered Network Models (Overview)

UDK 519.718

DOI 10.24412/2073-0667-2021-3-5-20

Network science is an essential tool for describing and analyzing complex systems in the social, biological, physical, information and engineering sciences. Initially, almost all studies on networks used an abstraction, in which systems are represented by an ordinary graph:

the vertices (or nodes) of the graph represent some entity or agent, and the connection between a pair of nodes is represented by an edge (or link''). Loops and multi-edges are usually ignored. Although this approach is rather naive, it has been extremely successful.

With the development of research on complex systems, it became necessary to move towards more complex and realistic models than a simple graph. For example, different heterogeneous properties of edges: they can be directional, have different strengths (i.e. weights), and exist only between nodes that belong to different sets (for example, bipartite networks), or be active only at a certain time. Much later, more and more efforts were made to investigate networks with multiple types of connections and the so-called networks of networks.

Such systems were explored decades ago in disciplines such as sociology and design, but relatively recently serious research has been carried out on multi-level complex networks and generalizations of terminology and tools in this area. One such generalization is the multilayer network model.

In telecommunication networks, problems naturally arise that are solved at several levels of the network. For example, the task of routing in a circuit-switched data network with several logical layers (different technologies) and different interfaces, which can lead to invalid paths. This paper shows a negative example of a graph with edge properties. The tasks of designing a multi-level WLAN structure, a two-level SDH / WDM network were solved, a scheme was developed to protect (restore) a two-level optical network. To assess the distribution of traffic, a two-level model (LCN) was introduced, consisting of physical and logical layers. All of these models are not universal (i.e. they either depend on a specific technology, or are applicable to specific types of networks, or only take into account connections between neighboring layers).

However, for modeling multi-level embedded networks of various natures, for more than 30 years several universities in Russia, Kyrgyzstan and Kazakhstan have been using the model of a hyper net and its development. Hyper nets make it possible to adequately describe multi-level networks with an arbitrary number of levels.

The hyper net (or S-hyper net) model consists of a physical layer and a logical layer(s) and is thus an abstraction of computer networks.

Probably the largest number of applications of the theory of hyper nets and S-hyper nets are in telecommunications and transport. Nevertheless, the theory of S-hyper nets is applicable to the analysis and synthesis of other systems of network structure.

The multilayer network model can be used to represent most types of complex systems (for example, in sociology, epidemiology, biomedicine, etc.) that consist of several networks or include disparate and / or multiple interactions between objects.

Modeling real networks with a more complex model than a graph has long been a necessity. However, questions about the unification of models, and most importantly, terminology began to arise only in the last decade. This article presents two of the most common layered networking models to date. It is shown that the choice of this or that model (even when solving the same problem) is based on the features of the network. For example, the application of the hyper-network model will most likely be appropriate when modeling a multi-layer telecommunication or transport network. A list of the studied literature was also given, as well as the author's works in the table, which showed the model's belonging to one or another class of multi-level networks.

Each of the above models describes only a subset of the set of multi-layer networks. It is possible to develop both existing models and introduce new ones for networks that are not yet integrated into the general theory. Therefore, research in this area will remain relevant and have many applications.

Key words: multilevel networks, multilayer networks, modeling, hyper nets.

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Bibliographic reference: Kalney A. M. Layered Network Models (Overview) //journal “Problems of informatics”. 2021, № 3 P. 5-20. DOI:10.24412/2073-0667-2021-3-5-20

D.A. Migov, A.N. *Korotkov

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia,

* Center for Project Development Petersburg Real Estate LLC, 196066, St. Petersburg, Russia

Cuts using for modeling the propagation of cascading failures in electrical power grids

UDK 621.311.1+519.17

DOI 10.24412/2073-0667-2021-3-21-33

Analysis of network reliability is extremely important for their design and operation. For various types of networks, various models have been proposed that take into account network particular features, within which different indicators of network reliability are considered. As a rule, random graphs in various modifications are taken as a basis. Usually, the probability of connectivity of the corresponding random graph in the case of unreliable edges that fail independently and absolutely reliable nodes is used as an indicator of network reliability.

The problems of exact calculating of various reliability indicators are NP-hard. When network elements are subject to dependent failure, reliability analysis becomes a much more time consuming task. A typical example of dependent network failures is cascading failures in power networks. The initiating event of the failure propagation process is caused by external circumstances: it can be a fallen tree, a strong gust of wind, a line break due to overload, etc. If its failure caused overloading of other lines or equipment, then this, in turn, can generate new outages, etc. Thus, a sequence of dependent failures occurs.

An important property of cascade outages in power grids is both their locality and their non-locality, as practice shows. The examples of real cascading outages show that there is a failure of lines along the sections that cut off certain subnets. Such scenarios for the propagation of cascading failures are explained by the fact that the failure of a power transmission line leads to an almost instantaneous redistribution of electricity to other power transmission lines, primarily to those lines that are included in the cut with the failed one. This paper proposes a model for the propagation of dependent failures in a network along its structural cuts.

As a structural model of a power grid, we consider an undirected graph G = (V, E), where V is the set of vertices, and E is the set of edges of the graph G. Let the presence probability be given for each edge. We will interpret this value as the probability of failure-free operation of the corresponding transmission line within a given time interval. If a failure occurs, then a cascade failure begins along the network cuts, the development of which is described by influence graphs. In this case, it is assumed that the vertices are absolutely reliable, i.e. are present with probability equal to 1.

In such conditions, several characteristics are considered as indicators of power network reliability: the probability of network connectivity, the probability of each consumer can connect to any power center, the probability of the availability of any power source for a given proportion of consumers. The last indicator can be more informative than the previous ones when considering a power grid of large dimension, for example, on a national scale, or several countries, if the corresponding networks are interconnected.

The article proposes an algorithm for the accurate calculation of reliability indices, based on the use of the total probability formula, and an estimation algorithm, based on the Monte Carlo approach. In addition, a method of cumulative updating of the bounds of reliability indicators is proposed, which makes it possible to make a conclusion about the sufficient reliability (or unreliability) of a network in relation to a given threshold.

The pseudocodes of the proposed algorithms and the results of numerical experiments are given.

Key words: power grid, reliability, cascading failure, dependent failure, influence graph.

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Bibliographic reference: Migov D.A., Korotkov A.N. Cuts using for modeling the propagation of cascading failures in electrical power grids //journal “Problems of informatics”. 2021, № 3 P. 21-33. DOI:10.24412/2073-0667-2021-3-21-33

V. A. Akishin

The Bonch-Bruevich Saint Petersburg State University of Telecommunications, 193232, Saint Petersburg, Russia

Cognitive model for evaluating customer experience in the structure of infocommunication landscape of a telecom operator

UDK 65.011.56

DOI 10.24412/2073-0667-2021-3-34-55

Modern telecommunication market is highly competitive and, as a result, there is a high cost of attracting new customers. In addition, players in the telecommunications market have leveled off in terms of provided services and prices. That is why, telecom operators are looking for new development scenarios and sources of income.

Customer Experience Management (CEM) is one of the promising development direction that can provide a competitive advantage for a telecom operator in today's telecommunications market. The CEM concept focuses on the customerand its (CEM) goals are to build long-term relationships with the customer. Such relationships are based on formation positive customer experience and impressions from interaction with a company. In other words, the CEM concept focuses on working with loyalty and customer retention.

The implementation of the CEM concept assumes that all operational processes of the company should be built taking into account the total customer experience. In other words, all functional blocks of the company (even blocks, which does not work directly with the customer) should focus on the experience and impressions of the company's customers. Such a customer-centric model of managing business of a telecom operator is impossible without measuring customer experience in the context of the main operational processes and business functions. In other words, a telecom operator must understand how certain operational processes (and their KQI/KPI) affect the total customer experience.

To solve the above problems, it is required to develop a decision support system (DSS), which aimed on improving quality of management decision related to customer experience. Such system should be based on a cognitive model for assessing customer experience. The cognitive model should provide an opportunity, on the one hand, to assess the dependence of customer experience from the efficiency of the company's operational processes, and, on the other hand, to simulate optimal scenarios for managing customer experience on the telecom operator's network.

The main task of the study is development of a cognitive model for assessing customer experience in the context of managing operational processes and the infocommunication landscape of a domestic telecom operator. To solve this problem, the author sequentially analyzes the subject area and develops a cognitive model for assessing customer experience in the structure of the operational processes of a domestic telecom operator.

In the first part of the work, the author investigates existing research and approaches in the Customer Experience domain. Special attention is paid to the research of TM Forum. In particular, the thesaurus and the basic concepts that TM Forum uses when working on the topic of Customer Experience are examined. In addition, TM Forum functional model for assessing the integral customer experience is considered - this model can provide an unified measure of CX for the telecommunications industry.

The second part of the work highlights the development of a model for assessing the integral customer experience based on fuzzy cognitive maps in the specifics of a domestic telecom operator. First of all, a functional representation of the model is formulated - value of the integral customer experience should be formed as a result of aggregation of metrics that affect the customer experience in the context of various dimensions of the company. Further, the issues of the mathematical basis of the model are highlighted, in particular, the basic calculations, which are required for solving research problem. For example, it is highlighted: calculation of integral customer experience in the context of the stages of the customer's lifecycle (based on the metrics of customer experience); calculation of integral customer experience for the entire customer lifecycle; calculation of the force of mutual influence between different factors of the model.

In the final part of the work, the author determines the practical value of the developed model (in particular, static and dynamic analysis of the model) for the tasks of managing the experience of the telecom operator's clients.

Key words: DSS, cognitive model, fuzzy cognitive maps, customer experience, operational processes.

References

1. GB962 Customer Experience Management Solution Suite. [Electron. Res.]: \url{https://www.tmforum.org/resources/suite/gb962-customer-experience-manage...} (date of access: 02.04.2020).

2. GB962 Customer Experience Management: Introduction and Fundamentals R16.0.1. [Electron. Res.]: \url{https://www.tmforum.org/resources/best-practice/gb962-customer-experienc...} (date of access: 02.04.2020).

3. GB962A Customer Experience Management Lifecycle Metrics R15.0.1. [Electron. Res.]: \url{https://www.tmforum.org/resources/best-practice/gb962a-customer-experien...} (date of access: 30.04.2020).

4. RN341 Customer Experience Management Index (CEMI) Release Notes R2.0. [Electron. Res.]: \url{https://www.tmforum.org/resources/reference/rn341-customer-experience-ma...} (date of access: 01.05.2020).

5. TMF066 Customer Experience Management Index Technical Specification V1.1. [Electron. Res.]: \url{https://www.tmforum.org/resources/technical-report-best-practice/tmf066-...} (date of access: 02.03.2020).

6. TR193 Customer Experience Management Index v1.3. [Electron. Res.]: \url{https://www.tmforum.org/resources/technical-report-best-practice/tr193-c...} (date of access: 01.05.2020).

7. Akishin V. A., Kislyakov S. V., Fenomenov M. A. // Funkczionalnaya arkhitektura CEM-kompleksa dlya vnedreniya v IT-landshaft krupnogo operatora svyazi // T-COMM: TELEKOMMUNIKACzII I TRANSPORT. 2016. N 10. S. 12-16.

8. Akishin V. A., Kislyakov S. V., Fenomenov M. A. // Polzovatelskij opyt v kognitivnoj modeli upravleniya setyu operatora // T-comm. 2016. T. 10, N 10.

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Bibliographic reference: Akishin V. A. Cognitive model for evaluating customer experience in the structure of infocommunication landscape of a telecom operator //journal “Problems of informatics”. 2021, № 3 P. 34-55. DOI:10.24412/2073-0667-2021-3-34-55

S. V. Bredikhin, V. M. Lyapunov, N. G. Scherbakova

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

The structure and parameters of the unweighted co-authorship network based on DB RePEc data

UDK 001.12+303.2

DOI 10.24412/2073-0667-2021-3-56-67

This work was carried out under state contract with ICMMG SB RAS (0251-2021-0005).

In this paper we investigate the structure of scientific collaboration based on co-authorship in publications retrieved from the RePEc database. The main attention is paid to the co-authorship network: nodes correspond to authors, and two authors are considered connected if they are coauthors of at least one publication. The network is represented by the undirected unweighted collaboration graph.

The co-authorship in articles provides a window on patterns of collaboration within the academic community. The study of scientific collaboration networks is carried out in two main directions. Empirical measurements provide detailed characteristics of the network: statistical properties of the distribution of degrees of nodes, global network parameters, nodes centralities (Newman 2001a; 2001b; 2001c). The study of the dynamic properties of real networks and network models makes it possible to identify the structural principles that govern the evolution of networks; dynamic properties, in turn, can explain static ones (Barabasi et al., 2001; Savic et al., 2017).

Co-authorship networks are used to identify research groups and the most important researchers and to predict their scientific success; to classify journals by degree of co-authorship and to make maps of science. Co-authorship networks can be constructed for different components of analysis, such as researchers, institutions, and countries. We use a researcher as the unit of analysis.

The articles used in this study were retrieved from the RePEc database (REPEC). In order to identify authors uniquely and to infer actual author identity we use the author profile that authors create basing on the Author Service provided by the RePEc database (similar to Google Scholar).

The study showed that in the collection of publications under consideration the fraction of coauthored publications is small (25\,\%) and the prevailing trend is the presence of two coauthors in a publication (77%). The most authors are indirectly connected to each other - the maximum component includes 90% of authors. The co-authorship network under consideration is scale-free and shows the small world effect.

Key words: bibliometrics, co-authorship, collaboration indices.

References

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9. Newman M.E.J. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality // Phys. Rev. E, 64(1), 016132.

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Bibliographic reference: S. V. Bredikhin, V. M. Lyapunov, N. G. Scherbakova The structure and parameters of the unweighted co-authorship network based on DB RePEc data//journal “Problems of informatics”. 2021, № 3 P. 56-67. DOI:10.24412/2073-0667-2021-3-56-67

V.V. Mironov

Ryazan State Radio Engineering University named after Academician V. F. Utkin, 390005, Ryazan, Russia

New scientific index of the author's publishing activity

UDK 001.5 + 519.24

DOI 10.24412/2073-0667-2021-3-68-78

A new (three-dimensional) scientometric index is presented - a (weighted) index of the author's publishing (or public) activity, depending on the number of author's publications in various journals. Informatively, the index of the author's publishing activity characterizes the breadth of the author's scientific interests, attention to his work on the part of publishers and (indirectly) the level of scientific significance of his results. A weighted estimate is achieved by accounting for the quartiles of the journals. For the convenience of comparing the publishing activity of scientists, a one-dimensional pseudo-norm (or assessment) of the activity index has been introduced. The new indices are easy to use, based on the Hirsch methodology already familiar to the scientific community, and at the same time, in quality they surpass the Hirsch methodology in analyzing the scientist's contribution to the development of his branch of science.

Key words: scientometric indicator, multidimensional and one-dimensional indices of the author's publishing activity, breadth of scientific interests, balanced and informative indices.

References

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Bibliographic reference: Mironov V.V. New scientific index of the author's publishing activity //journal “Problems of informatics”. 2021, № 3 P. 68-78. DOI:10.24412/2073-0667-2021-3-68-78

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