The article describes the original software tools for an experimental estimation of computational complexity of software solutions for problems on graph models of systems. The classes of the solved problems and the tools for analysis of results are listed. The method based on selection of graph models by their structural complexity is introduced.
Commonly in network analysis a graph (network) is represented by its adjacency matrix, and the latter may have an enormous order. We show that in many situations (generalizing the case of regular graph) a much smaller matrix (referred as type adjacency matrix) may be used instead. We introduce concepts of the types of nodes and of the type adjacency matrix, study properties of the latter and demonstrate some of its applications in social and economic network analysis. In particular, we consider centrality measures in undirected networks and dynamic patterns in a development model based on the structure of optimal paths in directed weighted networks.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.
пространственная структура РНК, мультиплет, стем, псевдоузел, линк, граф
A form for an unbiased estimate of the coefficient of determination of a linear regression model is obtained. It is calculated by using a sample from a multivariate normal distribution. This estimate is proposed as an alternative criterion for a choice of regression factors.