  How to identify influential nodes in complex networks is an important aspect
in the study of complex network. In this paper, a novel fuzzy local dimension
(FLD) is proposed to rank the influential nodes in complex networks, where a
node with high fuzzy local dimension has high influential ability. This
proposed method focuses on the influence of the distance from the center node
on the local dimension of center node by fuzzy set, resulting in a change in
influential ability. In order to show this proposed method's effectiveness and
accuracy, four real-world networks are applied in this paper. Meanwhile,
Susceptible-Infected (SI) is used to simulate the spreading process by FLD and
other centrality measures, and Kendall's tau coefficient is used to describe
the correlation between the influential nodes obtained by centrality and the
results measured by SI model. Observing from the ranking lists and simulated
results, this method is effective and accurate to rank the influential nodes.
