Online systems with the highest global audiences take form of widely-used social platforms. Their immense traffic resulted in increased attention from researchers into various phenomena including information propagation in social networks. Although there exist some libraries, such as igraph and netdep, which allow representation of graphs in the R language, due to continual appearance of new models and information spreading approaches, the researchers are usually forced to write their own scripts to perform actual simulations and study their results.
In this paper, the authors propose an object-oriented library and environment in R, for running simulation experiments focused on information spreading within complex networks. Object-oriented programming paradigms such as encapsulation, separation of concerns and modularity were used in the proposed software, to provide researchers with a scalable framework allowing quick and easy creation of experimental scenarios for studying information propagation in complex networks. It also supports new approaches, not available in other libraries, related to spreading seeds over the time in a form of sequential seeding, as well as coordinated execution, making it possible to compare algorithms in invariable experimental conditions.