Synchronization within the dynamical nodes of a complex network is usually considered homogeneous through all the nodes. Here we show, in contrast, that subsets of interacting oscillators may synchronize in different ways within a single network. This diversity of synchronization patterns is promoted by increasing the heterogeneous distribution of coupling weights and/or asymmetries in small networks. We also analyze consistency, defined as the persistence of coexistent synchronization patterns regardless of the initial conditions. Our results show that complex weighted networks display richer consistency than regular networks, suggesting why certain functional network topologies are often constructed when experimental data are analyzed. Dynamical systems may synchronize in several ways, at the same time, when they are coupled in a single complex network. Examples of this diversity of synchronization patterns may be found in research fields as diverse as neuroscience, climate networks, or ecosystems. Here we report the conditions required to obtain coexisting synchronizations in arrangements of interacting chaotic oscillators, and relate these conditions to the distribution of coupling weights and asymmetries in complex networks. We also analyze the conditions required for a high statistical occurrence of the same synchronization patterns, regardless of the oscillators' initial conditions. Our results show that these persistent synchronization patterns are statistically more frequent in complex weighted networks than in regular ones, explaining why certain functional network topologies are often retrieved from experimental data. Besides, our results suggest that considering both the different coexisting synchronizations and also their statistics may result in a richer understanding of the relations between functional and structural networks of oscillators.

This work was partially supported by the Spanish Ministry of Economy and Competitiveness and FEDER (Project No. FIS2015-66503). AEPV acknowledges support from the Swiss National Science Foundation Project NEURECA (CR13I1 138032). J.G.O. also acknowledges support from the the Generalitat de Catalunya (Project No. 2014SGR0947), the ICREA Academia Programme, and from the “María de Maeztu” Programme for Units of Excellence in R&D (Spanish Ministry of Economy and Competitiveness, MDM-2014-0370).