  Often exhibiting hierarchical and overlapping structures, communities or
modular groups are fundamental and complex in network science. One of the most
exploited tools to detect the mesoscopic structure is synchronization. Several
phenomena including convergence rate and local convergence under constraints
are studied to uncover the existence and features of communities. Here,
employing a background of opinion dynamics, we study the divergence of agents'
opinions, i.e. the state differences of nodes, in the progress of reaching
global consensus and then reveal its connections with hierarchy and overlap in
the modular structure. Furthermore, based on the obtained close relationships,
a new method is proposed to identify hierarchical and overlapping communities,
whose robustness and efficiency are validated via experiments on real and
artificial networks. Both the connections and approaches provide a novel
insight on the features of modular structures in networks.
