  We introduce the Multiscale Voter Model (MVM) to investigate clan influence
at multiple scale -- family, neighborhood, political party... -- in opinion
formation on real complex networks. Clans, consisting of similar nodes, are
constructed using a coarse-graining procedure on network embeddings that allows
us to control for the length scale of interactions. We ran numerical
simulations to monitor the evolution of MVM dynamics in real and synthetic
networks, and identified a transition between a final stage of full consensus
and one with mixed binary opinions. The transition depends on the scale of the
clans and on the strength of their influence. We found that enhancing group
diversity promotes consensus while strong kinship yields to metastable clusters
of same opinion. The segregated domains, which signal opinion polarization, are
discernible as spatial patterns in the hyperbolic embeddings of the networks.
Our multiscale framework can be easily applied to other dynamical processes
affected by scale and group influence.
