Modeling Network Evolution by Colored Petri Nets

Abstract

Discovering how information was distributed was essential for tracking, optimizing, and controlling networks. In this paper, a premier approach to analyze the reciprocity of user behavior, content, network structure, and interaction rules to the interplay between information diffusion and network evolution was proposed. Parameterization and insight diffusion patterns were characterized based on the community structure of the underlying network using diffusion related behavior data, collected by a developed questionnaire. The user roles in creating the flow of information were stochastically modeled and simulated by Colored Petri Nets, where the growth and evolution of the network structure was substantiated through the formation of the clustering coefficient, the average path length, and the degree distribution. This analytical model could be used for various tasks, including predicting future user activities, monitoring traffic patterns of networks, and forecasting the distribution of content.