识别复杂网络中的有影响力节点：一种节点信息维度方法。

Identifying influential nodes in complex networks: A node information dimension approach.

作者信息

Bian Tian, Deng Yong

机构信息

Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu 610054, China.

出版信息

Chaos. 2018 Apr;28(4):043109. doi: 10.1063/1.5030894.

DOI:10.1063/1.5030894

PMID:31906639

Abstract

In the field of complex networks, how to identify influential nodes is a significant issue in analyzing the structure of a network. In the existing method proposed to identify influential nodes based on the local dimension, the global structure information in complex networks is not taken into consideration. In this paper, a node information dimension is proposed by synthesizing the local dimensions at different topological distance scales. A case study of the Netscience network is used to illustrate the efficiency and practicability of the proposed method.

摘要

在复杂网络领域，如何识别有影响力的节点是分析网络结构中的一个重要问题。在现有的基于局部维度识别有影响力节点的方法中，没有考虑复杂网络中的全局结构信息。本文通过综合不同拓扑距离尺度下的局部维度，提出了一种节点信息维度。以Netscience网络为例，说明了所提方法的有效性和实用性。