Design of nodes importance assessment method for complex network based on neighborhood information

Xing LI; Jie ZHAN; Baoquan REN; Siqi ZHU

doi:10.11959/j.issn.2096-3750.2024.00335

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Design of nodes importance assessment method for complex network based on neighborhood information

Theory and Technology | 更新时间：2024-06-05

- Design of nodes importance assessment method for complex network based on neighborhood information
- Chinese Journal on Internet of Things Vol. 8, Issue 1, Pages: 49-59(2024)
- 作者机构：
  
  1. 湖南科技大学物理与电子科学学院，湖南湘潭 411201
  2. 中国电子设备系统工程公司研究所，北京 100141
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2024.00335
  CLC： O157.5
- Revised：2024-01-24，
  
  Online First：2024-03，
  
  Published：30 March 2024
- 稿件说明：
移动端阅览
Xing LI, Jie ZHAN, Baoquan REN, et al. Design of nodes importance assessment method for complex network based on neighborhood information[J]. Chinese Journal on Internet of Things, 2024, 8(1): 49-59.
DOI：

Xing LI, Jie ZHAN, Baoquan REN, et al. Design of nodes importance assessment method for complex network based on neighborhood information[J]. Chinese Journal on Internet of Things, 2024, 8(1): 49-59. DOI： 10.11959/j.issn.2096-3750.2024.00335.

摘要

在复杂网络中准确识别影响力节点，对网络管理和网络安全至关重要。局部中心性方法简明易用，但忽略了邻居节点间的拓扑关系，不能提供全局最优结果。提出了一种关联节点连边关系和拓扑结构的重要节点评估方法，该方法首先综合应用H指数和信息熵对节点进行评估，在此基础上代入节点的结构洞特征，即在关注节点自身质量和邻居节点信息量的同时，考虑了其“桥接”属性。采用疾病传播模型对算法进行验证，应用肯德尔（Kendall）相关系数、互补累积分布函数以及传播影响力来验证算法的有效性与适用性。在6个真实网络数据集上的仿真结果表明，在识别和排序网络中的关键节点上，所提方法比传统中心性方法更准确。

Abstract

Accurate identification of influential nodes in complex networks is crucial for network management and network security.The local centrality method is concise and easy to use

but ignores the topological relationship between neighboring nodes and cannot provide globally optimal results.A node importance assessment method was proposed to correlate the node edge relationship and topology

which firstly applied the H-index and information entropy to assess the nodes

then combined the structural holes of the nodes with the structural characteristics of the nodes

and took into account the attribute of “bridging” while focusing on the node’s own quality and the amount of information about the neighboring nodes.The algorithm was validated by simulating the propagation process using the SIR model

and the Kendall correlation coefficient

complementary cumulative distribution function and propagation influence were applied to validate the validity and applicability of the method.Comparison of the experimental results on six real network datasets shows that the proposed method is more accurate than the traditional centrality methods in identifying and ordering the key nodes in the network.

关键词

Keywords

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