MuNeG

Abstract

It is a common problem that cost of extracting data for network analysis could be very high. Also sometimes in the Internet is it hard to find graph with desired features such as node degree or clustering level. Because of that graph generators can than be very helpful. In the past bunch of models of such generators was developed: random graphs, small worlds and scale free networks. All of these generators were developed to quickly and efficiently create networks with desired parameters. However all of this models produce single layer graphs. Domain of multiplexes or multilayer graphs has not already been so deeply analysed, also because it is hard to collect multilayer data among real datasets or there is hard to define what kind of information layers exactly should represent. Proposed MuNeG - Multilayer Network Generator can produce, based on set of input parameters, multiplex networks - networks where each node has its counterpart in each layer. The carried out experiments proved that MuNeG graphs have different network and social parameters depends on input values. This feature gives user a very handful tool to generate multiplex networks on purpose of social network or complex network analysis. Generator features, input parameters and their influence on so called graph theory measures such as: node degree, average shortest path, diameter or clustering are described in the following article.