Redukce bezeškálových grafů pomocí genetických algoritmů

Abstract

This master's thesis describes design and implementation of Genetic Algorithm, which is able to create a representative sample from Scale-free network. Scale-free networks can be found in many fields e.g. sociology, biology or computer science. Main characteristic properties of Scale-free networks are that degree distribution follows power-laws and in network exists few highly connected centers. There are many known algorithms to compute interesting measures e.g. centrality, shortest paths etc. but some of them are impractical for large networks. Also in many applications it is needed to run expensive algorithms e.g. simulations of internet routing protocols. This is the reason why sampling from graph is essential. The method which is described in this thesis is based on theory of Genetic Algorithms. Reduced network should have similar properties as the original network, for example shape of degree distribution.