  The identification of influential spreaders in complex networks is a popular
topic in studies of network characteristics. Many centrality measures have been
proposed to address this problem, but most have limitations. In this paper, a
method for identifying influencers in complex networks via the local
information dimensionality. The proposed method considers the local structural
properties around the central node; therefore, the scale of locality only
increases to half of the maximum value of the shortest distance from the
central node. Thus, the proposed method considers the quasilocal information
and reduces the computational complexity. The information (number of nodes) in
boxes is described via the Shannon entropy, which is more reasonable. A node is
more influential when its local information dimensionality is higher. In order
to show the effectiveness of the proposed method, five existing centrality
measures are used as comparison methods to rank influential nodes in six real
world complex networks. In addition, a susceptible infected (SI) model and
Kendall's tau coefficient are applied to show the correlation between different
methods. Experiment results show the superiority of the proposed method.
