基于复杂网络动力学模型的链路预测方法

潘永昊; 于洪涛; 吴翼腾

doi:10.11959/j.issn.2096-109x.2019065

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基于复杂网络动力学模型的链路预测方法

学术论文 | 更新时间：2024-06-03

- 基于复杂网络动力学模型的链路预测方法
- Link prediction method based on complex network dynamics model
- 网络与信息安全学报 2019年5卷第6期页码：67-74
- 作者机构：
- 作者简介：
  
  [ "潘永昊（1992- ），男，甘肃金昌人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为复杂网络、链路预测" ]
  [ "于洪涛（1970- ），男，辽宁丹东人，博士，国家数字交换系统工程技术研究中心研究员，主要研究方向为网络大数据分析与处理" ]
  [ "吴翼腾（1992- ），男，山东乐陵人，国家数字交换系统工程技术研究中心博士生，主要研究方向为复杂网络链路预测、对抗样本等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61803384)
- DOI：10.11959/j.issn.2096-109x.2019065
  中图分类号： TP393
- 网络首发：2019-12，
  
  纸质出版：2019-12-15
- 稿件说明：
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潘永昊, 于洪涛, 吴翼腾. 基于复杂网络动力学模型的链路预测方法[J]. 网络与信息安全学报, 2019,5(6):67-74.

Yonghao PAN, Hongtao YU, Yiteng WU. Link prediction method based on complex network dynamics model[J]. Chinese Journal of Network and Information Security, 2019, 5(6): 67-74.
潘永昊, 于洪涛, 吴翼腾. 基于复杂网络动力学模型的链路预测方法[J]. 网络与信息安全学报, 2019,5(6):67-74. DOI： 10.11959/j.issn.2096-109x.2019065.

Yonghao PAN, Hongtao YU, Yiteng WU. Link prediction method based on complex network dynamics model[J]. Chinese Journal of Network and Information Security, 2019, 5(6): 67-74. DOI： 10.11959/j.issn.2096-109x.2019065.

摘要

链路预测是复杂网络中研究缺失连边和未来形成连边的重要组成部分，当前基于网络结构的链路预测方法成果丰富，而基于复杂网络动力学模型的链路预测研究较少。针对无权无向网络，首先构建了复杂网络动力学模型，然后给出了基于复杂网络动力学模型的链路预测节点中心性的量化评价指标，最后通过给出的节点中心性量化指标，提出了由复杂网络动力学模型定义的链路预测方法。通过在真实网络数据集上进行的实验表明，提出的链路预测方法较基准方法有明显的预测精度的提升。

Abstract

Link prediction is an important part of the study of missing links and future formations in complex networks.Currently

network structure-based link prediction methods are rich in results.Research on link prediction based on complex network dynamics model is rare.Firstly

a complex network dynamics model for unlicensed and undirected networks was constructed.Then the quantitative evaluation index of the link prediction node centrality based on the complex network dynamics model was given.Finally

the link prediction method defined by the complex network dynamics model was proposed by the given node centrality quantitative index.Experiments on real network datasets show that the proposed link prediction method has obvious prediction accuracy improvement.

关键词

Keywords

references

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