基于神经网络的链路预测算法

潘永昊; 于洪涛; 刘树新

doi:10.11959/j.issn.2096-109x.2018049

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基于神经网络的链路预测算法

论文 | 更新时间：2024-06-03

- 基于神经网络的链路预测算法
- Neural network-based link prediction algorithm
- 网络与信息安全学报 2018年4卷第7期页码：30-38
- 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。" ]
  [ "刘树新（1987-），男，山东潍坊人，博士，国家数字交换系统工程技术研究中心助理研究员，主要研究方向为复杂网络、网络信息挖掘。" ]
- 基金信息：
  
  国家自然科学基金创新研究群体基金资助项目;The Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61521003);国家自然科学基金资助项目;The National Natural Science Foundation of China(61601513)
- DOI：10.11959/j.issn.2096-109x.2018049
  中图分类号： TN94;TP3;TP391
- 网络首发：2018-07，
  
  纸质出版：2018-07-15
- 稿件说明：
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潘永昊, 于洪涛, 刘树新. 基于神经网络的链路预测算法[J]. 网络与信息安全学报, 2018,4(7):30-38.

Yonghao PAN, Hongtao YU, Shuxin LIU. Neural network-based link prediction algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(7): 30-38.
潘永昊, 于洪涛, 刘树新. 基于神经网络的链路预测算法[J]. 网络与信息安全学报, 2018,4(7):30-38. DOI： 10.11959/j.issn.2096-109x.2018049.

Yonghao PAN, Hongtao YU, Shuxin LIU. Neural network-based link prediction algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(7): 30-38. DOI： 10.11959/j.issn.2096-109x.2018049.

摘要

针对当前基于网络拓扑结构相似性的链路预测算法普遍存在精确度较低且适应性不强的问题，研究发现融合算法能够有效改善这些问题。提出了一种基于神经网络的融合链路预测算法，主要通过神经网络对不同链路预测相似性指标进行融合。该算法使用神经网络对不同相似性指标的数值特征进行学习，同时采用标准粒子群算法对神经网络进行了优化，并通过优化学习后的神经网络模型计算出融合指标。多个真实网络数据集上实验表明，该算法的预测精度明显高于融合之前的各项指标，并且优于现有融合方法的精度。

Abstract

To improve the difference existed in the link prediction accuracy and adaptability of different topology structure similarity based methods

a neural network-based link prediction algorithm

which fused similarity indices by neural network was proposed.The algorithm uses neural network to study the numerical characteristics of different similarity indices

and uses particle swarm optimization to optimize the neural network

and calculates the fusion index by the optimized neural network model.The experiment on the real network data set shows that the prediction accuracy of the algorithm is obviously higher than that before the fusion

and the accuracy is better than the existing methods.

关键词

Keywords

references

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