  Influence maximization aims to identify a set of influential individuals,
referred to as influencers, as information sources to maximize the spread of
information within networks, constituting a vital combinatorial optimization
problem with extensive practical applications and sustained interdisciplinary
interest. Diverse approaches have been devised to efficiently address this
issue, one of which involves selecting the influencers from a given centrality
ranking. In this paper, we propose a novel optimization framework based on
ranking basic cycles in networks, capable of selecting the influencers from
diverse centrality measures. The experimental results demonstrate that,
compared to directly selecting the top-k nodes from centrality sequences and
other state-of-the-art optimization approaches, the new framework can expand
the dissemination range by 1.5 to 3 times. Counterintuitively, it exhibits
minimal hub property, with the average distance between influencers being only
one-third of alternative approaches, regardless of the centrality metrics or
network types. Our study not only paves the way for novel strategies in
influence maximization but also underscores the unique potential of
underappreciated cycle structures.
