Software & Systems

Articles of journal № 2 at 2020 year.

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21. Comparative analysis of community identification algorithms in complex network systems using social networks as an example [№2 за 2020 год]
Authors: Kochkarov A.A., N.V. Kalashnikov , R.A. Kochkarov
Visitors: 3030
The paper considers the identifying community in social networks. There is a graphical approach to the study of social networks. There is a comparative analysis of the basic algorithms and the aggregate al-gorithm proposed by the authors. To test the algorithms, the authors generated graphs initially with different noise levels and gave a community number. To compare graph partitions, two well-known metrics the authors used – Normal-ized Mutual Information (NMI) and Split join distance. Each of the metrics has its own advantages. To verify the basic algorithms, and analysis the authors made of the Facebook social network geo-graphic for the community presence in them and tested the aggregated MetaClust algorithm. The pro-posed MetaClust algorithm showed high performance compared to the base ones. The modularity val-ues for its partitions (on average) are higher compared to the basic algorithms. Also, the algorithm qual-ity can be judged by the absence of a “tail” modularity in the distribution. The average results shown by the algorithms on the generated graphs correspond to the application results on the ego networks. To generate model data, it seems appropriate to use pre-fractal graphs and a wider class of dynamic graphs. The sequence of generated community graphs corresponds to the dynamic graph trajectory, the communities are seeds and blocks, and the noise is the addition of the new edge different ranks be-tween the seeds. The next step is a formal description of the graphs’ noise in the class terminology of the dynamic and pre fractal graphs. Using the pre fractal graph class will allow us to calculate the structural charac-teristics and of graph properties and communities in them.

22. Requirements for the software environment of automated flight data analysis [№2 за 2020 год]
Authors: E.M. Eremin , A.V. Nagornov
Visitors: 2080
In aircraft tests, as well as in flight researches and semi-natural experiments on aviation issues since the middle of the last century, there is an automated processing problem for the tabular time series of experimental data. Modern data analysis software provides a solution to the flight information analysis problems, but a universal software environment for these purposes does not exist: aviation profession-als encounter with the necessity to use several different applications, which slows down the work. An urgent problem is to create a specialized software environment focused on aviation topic needs. To produce the requirements for the new software environment, there are the working typical ele-ments with flight information: data filtering according to specified criteria, process dynamics visualiza-tion on the graph of parameter dependence on time, time-series derivatives addition, data visualization on the graph of parameter dependence on another parameter, determination of the generalizing calcu-lated indicator value on the entire record or its fragment basis, relationship determination between pa-rameters, multiple linear constructions and nonlinear regression models. For each of the standard elements, there is a practical work example with the performing experiment results. There are also existing application examples that solve specific problems with acceptable or maximum possible quality. This paper contains the system general architecture. The authors proposed the use of the model–view–controller scheme, development in the C++ programming language using the object-oriented pro-gramming patterns observer, linker and strategy, as well as the Row class basic code, each object of which contains one data table column. The quantitative data of a table column in an object of the Row class is in the standard C++ vector container, which contains double type elements.

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