Dimension reduction of dynamics on modular and heterogeneous directed networks.

ABSTRACT: Dimension reduction is a common strategy to study nonlinear dynamical systems composed by a large number of variables. The goal is to find a smaller version of the system whose time evolution is easier to predict while preserving some of the key dynamical features of the original system. Finding such a reduced representation for complex systems is, however, a difficult task. We address this problem for dynamics on weighted directed networks, with special emphasis on modular and heterogeneous networks. We propose a two-step dimension-reduction method that takes into account the properties of the adjacency matrix. First, units are partitioned into groups of similar connectivity profiles. Each group is associated to an observable that is a weighted average of the nodes' activities within the

SUBMITTER: Vegue M 

PROVIDER: S-EPMC10198746 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature