The important nodes identification has been an interesting problem in this issue. Several centrality methods have been proposed to solve this problem, but most previous methods have their own limitations. To address this problem more effectively, multi-local dimension (MLD) which is based on the fractal property is proposed to identify the vital spreaders in this paper. This proposed method considers the information contained in the box and $q$ plays a weighting coefficient for this partition information. MLD would have different expressions with different values of $q$, and it would degenerate to local information dimension and variant of local dimension when $q=1$ when $q=0$ respectively, both of which have been effective identification methods for influential nodes. Thus, MLD would be a more general method which can degenerate to some exiting centrality methods. In addition, different from classical methods, the node with low MLD would be more important in the network. Some real-world and theoretical complex networks and comparison methods are applied in this paper to show the effectiveness and reasonableness of this proposed method. The experiment results show the superiority of this proposed method.

重要节点标识已成为此问题中的一个有趣问题。已经提出了几种集中性方法来解决这个问题，但是大多数先前的方法都有其自身的局限性。为了更有效地解决这个问题，本文提出了一种基于分形特性的多局部维数（MLD）来识别生命传播器。此提议的方法考虑了框中包含的信息，并且$q$对该分区信息起加权系数。MLD将具有不同的表达式，并且具有不同的值$q$，并且在使用时会退化为本地信息维度和本地维度的变体 $q=\mathrm{1个}$ 什么时候 $q=0$分别是影响节点的有效识别方法。因此，MLD将是一种更通用的方法，可以退化为某些现有的中心方法。另外，与传统方法不同，具有低MLD的节点在网络中将更为重要。本文采用了一些实际的，理论上复杂的网络和比较方法，以证明该方法的有效性和合理性。实验结果表明了该方法的优越性。