融合高阶信息的社交网络重要节点识别算法

闫光辉; 张萌; 罗浩; 李世魁; 刘婷

doi:10.11959/j.issn.1000-436x.2019198

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融合高阶信息的社交网络重要节点识别算法

学术论文 | 更新时间：2024-06-05

- 融合高阶信息的社交网络重要节点识别算法
- Identifying vital nodes algorithm in social networks fusing higher-order information
- 通信学报 2019年40卷第10期页码：109-118
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730070
- 作者简介：
  
  [ "闫光辉（1970- ），男，河南睢县人，博士，兰州交通大学教授、博士生导师，主要研究方向为数据库理论与系统、物联网工程与应用、数据挖掘、复杂网络分析等。" ]
  [ "张萌（1995- ），女，山西芮城人，兰州交通大学硕士生，主要研究方向为社交网络分析、数据挖掘等。" ]
  [ "罗浩（1988- ），男，山西原平人，兰州交通大学硕士生，主要研究方向为数据挖掘、多关系网络分析等。" ]
  [ "李世魁（1994- ），男，甘肃民勤人，兰州交通大学硕士生，主要研究方向为数据挖掘、复杂网络分析等。" ]
  [ "刘婷（1993- ），女，甘肃陇西人，兰州交通大学硕士生，主要研究方向为数据挖掘、信息安全等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61662066);国家自然科学基金资助项目(61163010);甘肃省青年基金资助项目(1606RJYA222)
- DOI：10.11959/j.issn.1000-436x.2019198
  中图分类号： TP181，TP391
- 网络出版日期：2019-10，
  
  纸质出版日期：2019-10-25
- 稿件说明：
移动端阅览
闫光辉, 张萌, 罗浩, 等. 融合高阶信息的社交网络重要节点识别算法[J]. 通信学报, 2019,40(10):109-118.

Guanghui YAN, Meng ZHANG, Hao LUO, et al. Identifying vital nodes algorithm in social networks fusing higher-order information[J]. Journal on communications, 2019, 40(10): 109-118.
闫光辉, 张萌, 罗浩, 等. 融合高阶信息的社交网络重要节点识别算法[J]. 通信学报, 2019,40(10):109-118. DOI： 10.11959/j.issn.1000-436x.2019198.

Guanghui YAN, Meng ZHANG, Hao LUO, et al. Identifying vital nodes algorithm in social networks fusing higher-order information[J]. Journal on communications, 2019, 40(10): 109-118. DOI： 10.11959/j.issn.1000-436x.2019198.

摘要

识别重要节点是复杂网络研究的基础性问题。现有理论框架主要以“点-边”这种低阶结构为基本单元，往往忽略了多个节点之间可能存在的交互性、传递性等重要因素。为了更加精确地识别重要节点，对网络中以模体为基本单元的高阶结构进行了研究，首先，提出了节点高阶度的概念，进一步引入证据理论融合了节点的高阶结构和低阶结构信息，设计了一种融合节点高阶信息的半局部重要节点识别方法。在3个真实社交网络上的实验结果表明，相较于只关注低阶结构的已有方法，所提出的算法能够更加精确地识别网络中的重要节点。

Abstract

Identifying vital nodes is a basic problem in complex network research.The existing theoretical framework

mainly considered from the lower-order structure of node-based and edge-based relations often ignores important factors such as interactivity and transitivity between multiple nodes.To identify vital nodes more accurately

the motif

the high-er-order structure of the network

was studied as the basic unit.Firstly

a notion of higher-order degree of nodes in a com-plex network was proposed.Then

the higher-order structure and lower-order structure of nodes were fused into evidence theory.A semi-local identifying vital nodes algorithm fusing higher-order information of nodes was designed.The results of experiments on three real social networks show that the proposed algorithm can identify vital nodes more accurately in the network than the existing methods which only focus on the low-order structure.

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