  The problem of influence maximization, i.e., finding the set of nodes having
maximal influence on a network, is of great importance for several
applications. In the past two decades, many heuristic metrics to spot
influencers have been proposed. Here, we introduce a framework to boost the
performance of any such metric. The framework consists in dividing the network
into sectors of influence, and then selecting the most influential nodes within
these sectors. We explore three different methodologies to find sectors in a
network: graph partitioning, graph hyperbolic embedding, and community
structure. The framework is validated with a systematic analysis of real and
synthetic networks. We show that the gain in performance generated by dividing
a network into sectors before selecting the influential spreaders increases as
the modularity and heterogeneity of the network increase. Also, we show that
the division of the network into sectors can be efficiently performed in a time
that scales linearly with the network size, thus making the framework
applicable to large-scale influence maximization problems.
