Simulating the dynamics of scale-free networks via optimization.

ABSTRACT: We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real syst

SUBMITTER: Schieber TA 

PROVIDER: S-EPMC3865918 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature