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(.):  computational complexity assessment of graph algorithms, average path length, average number of paths in a graph
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(.):  One of the important stages in developing an algorithm to solve a given problem is assessing its computational complexity. The computational complexity of an algorithm is usually evaluated as the dependence of the growth rate of its running time on the size of the input data. Such an assessment makes it possible to compare the performance of algorithms that solve the same problem, regardless of the hardware and software platform. This work examines several algorithms designed to solve problems for engineering networks modeled as graphs. In studies dedicated to these algorithms, their computational complexity is not provided. It is generally accepted that the input size of graph algorithms is evaluated based on the number of vertices and edges. What the considered algorithms have in common is that their computational complexity depends not only on the number of vertices and edges, but also on the path length and the number of paths between two vertices. The study provides estimates for the dependence of the average number of paths and the average path length between two vertices in engineering network graphs. Using these estimates, an analysis was performed for one of the investigated algorithms, which addresses the problem of identifying critical nodes in a transportation network. The principle of operation of this algorithm is to reduce the problem to an equivalent integer linear programming problem. Estimates were obtained for how the number of variables and constraints depends on the number of nodes in the transportation network.

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