Sergiy Butenko

TL;DR: This work conducts the statistical analysis of this graph and shows that it follows the power-law model, and detects cliques and independent sets in this graph, which allows one to apply a new data mining technique of classifying financial instruments based on stock prices data, which provides a deeper insight into the internal structure of the stock market.

TL;DR: An algorithmic approach is developed exploiting the graph-theoretic properties of a k-plex that is effective in solving the problem to optimality on very large, sparse graphs such as the power law graphs frequently encountered in the applications of interest.

TL;DR: In this paper, the authors present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed.

TL;DR: The evolution of the structural properties of the market graph over time is studied and conclusions regarding the dynamics of the stock market development are drawn based on the interpretation of the obtained results.

TL;DR: The proposed article includes an introduction to the underlying biochemistry and genomic aspects of the problems as well as to the graph-theoretic aspects ofThe solution approaches, which describes a particular type of problem, and gives an example to show how the graph model can be derived.