Research on HMM based link prediction method in heterogeneous network

Rong QIAN; Jianting XU; Kejun ZHANG; Hongyu DONG; Fangyuan XING

doi:10.11959/j.issn.1000-436x.2022095

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Research on HMM based link prediction method in heterogeneous network

Correspondences | 更新时间：2024-06-05

- Research on HMM based link prediction method in heterogeneous network
- Journal on Communications Vol. 43, Issue 5, Pages: 214-225(2022)
- 作者机构：
  
  1. 北京电子科技学院网络空间安全系，北京 100070
  2. 西安电子科技大学计算机科学与技术学院，陕西西安 710071
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2018YFB1004101)
- DOI：10.11959/j.issn.1000-436x.2022095
  CLC： TP181
- Online First：2022-05，
  
  Published：25 May 2022
- 稿件说明：
移动端阅览
Rong QIAN, Jianting XU, Kejun ZHANG, et al. Research on HMM based link prediction method in heterogeneous network[J]. Journal on Communications, 2022, 43(5): 214-225.
DOI：

Rong QIAN, Jianting XU, Kejun ZHANG, et al. Research on HMM based link prediction method in heterogeneous network[J]. Journal on Communications, 2022, 43(5): 214-225. DOI： 10.11959/j.issn.1000-436x.2022095.

摘要

为了解决异质网络的结构信息和语义信息挖掘不全面的问题，针对异质网络的链接预测，提出了将基于元路径的分析方式与隐马尔可夫模型相结合的链接预测方法。考虑到聚簇可以有效地捕获异质网络的结构信息，将k-means算法进行改进得到基于距离均方差最小的初始聚簇中心方法，并将其应用到隐马尔可夫模型（HMM）中，设计了基于聚簇的一阶隐马尔可夫模型（C-HMM

(1)

）的链接预测方法，同时提出基于聚簇的二阶隐马尔可夫模型（C-HMM

(2)

）的异质网络的链接预测方法。进一步考虑数据的特征信息，提出了将最大熵模型和二阶隐马尔可夫模型相结合的链接预测方法ME-HMM。实验结果表明，ME-HMM比C-HMM方法的链接预测精确度更高，且ME-HMM因充分考虑到数据的特征信息比C-HMM的性能更加优异。

Abstract

In order to solve the problem that incomplete mining of structural information and semantic information in heterogeneous networks

a link prediction method combining meta-path-based analysis and hidden Markov model was proposed for link prediction of heterogeneous network.Considering that clustering could effectively capture the structural information of heterogeneous network

the k-means algorithm was improved to obtain the initial clustering center method based on the minimum distance mean square error

and it was applied to the hidden Markov model

first-order cluster hidden markov model (C-HMM

(1)

) link prediction method

and a link prediction method for heterogeneous network with second-order cluster hidden Markov model (C-HMM

(2)

) were designed.Further

considering the feature information of the data

a link prediction method called ME-

HMM that combined the maximum entropy model and the second-order Markov model was proposed.The experimental results show that the ME-HMM has higher link prediction accuracy than the C-HMM

and the ME-HMM method has better performance than the C-HMM method because it fully considers the feature information of the data.

关键词

Keywords

references

LEE J B , ADORNA H . Link prediction in a modified heterogeneous bibliographic network [C ] // Proceedings of 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining . Piscataway:IEEE Press , 2012 : 442 - 449 .

蒋宗礼 , 陈浩强 , 张津丽 . 基于融合元路径权重的异质网络表征学习 [J ] . 计算机系统应用 , 2019 , 28 ( 12 ): 28 - 36 .

JIANG Z L , CHEN H Q , ZHANG J L . Heterogeneous network representation learning based on fusion meta-path weights [J ] . Computer Systems ＆ Applications , 2019 , 28 ( 12 ): 28 - 36 .

MA Y , CHENG G Q , LIANG X X , et al . Heterogeneous graph neural networks based on meta-path [C ] // Proceeding of 2020 3rd International Conference on Algorithms,Computing and Artificial Intelligence .[S.l.:s.n. ] , 2020 : 95 - 98 .

朱恺 . 异构社交网络中基于元路径的链接预测研究 [D ] . 南京:南京大学 , 2020 .

ZHU K . Research on meta path-based link prediction in heterogeneous social networks [D ] . Nanjing:Nanjing University , 2020 .

郑玉艳 . 异质信息网络的语义元路径分析方法研究 [D ] . 北京:北京邮电大学 , 2019 .

ZHENG Y Y . Research on semantic meta path analysis method of heterogeneous information networks [D ] . Beijing:Beijing University of Posts and Telecommunications , 2019 .

孙艺洲 , 韩家炜 . 异构信息网络挖掘:原理和方法 [M ] . 段磊,朱敏,唐常杰,等,译 . 北京 : 机械工业出版社 , 2017 .

SUN Y Z , HAN J W . Mining heterogeneous information networks:principles and methodologies [M ] . Translated by DUAN L,ZHU M,TANG C J,et al . Beijing : China Machine Press , 2017 .

SUN Y , HAN J , YAN X , et al . PathSim:meta path based top-k similarity search in heterogeneous information networks [J ] . Proceedings of the VLDB Endowment , 2011 , 4 ( 11 ): 992 - 1003 .

郭振宏 , 李海峰 . 异质信息网络中演员合作关系的链路预测 [J ] . 计算机工程 , 2017 , 43 ( 1 ): 219 - 225 .

GUO Z H , LI H F . Link prediction of actor cooperation relationship in heterogeneous information network [J ] . Computer Engineering , 2017 , 43 ( 1 ): 219 - 225 .

韩忠明 , 李胜男 , 郑晨烨 , 等 . 基于动态网络表示的链接预测 [J ] . 物理学报 , 2020 , 69 ( 16 ): 332 - 345 .

HAN Z M , LI S N , ZHENG C Y , et al . Link prediction model based on dynamic network representation [J ] . Acta Physica Sinica , 2020 , 69 ( 16 ): 332 - 345 .

刘大伟 , 吕元娜 , 余智华 . 一种改进的复杂网络链路预测算法 [J ] . 小型微型计算机系统 , 2016 , 37 ( 5 ): 1071 - 1074 .

LIU D W , LV Y N , YU Z H . An improved link prediction algorithm for complex networks [J ] . Journal of Chinese Computer Systems , 2016 , 37 ( 5 ): 1071 - 1074 .

董鑫 . 基于 Boosting 的异质信息网络链路预测方法研究 [D ] . 黑龙江:哈尔滨工程大学 , 2017 .

DONG X . Research on boosting based method of link prediction in heterogeneous information network [D ] . Heilongjiang:Harbin Engineering University , 2017 .

赵妍 , 赵书良 , 马秋微 . 基于图核的异质信息网络链路预测方法 [J ] . 计算机应用研究 , 2021 , 38 ( 10 ): 3125 - 3130 .

ZHAO Y , ZHAO S L , MA Q W . Graph kernel based link prediction in heterogeneous information network [J ] . Application Research of Computers , 2021 , 38 ( 10 ): 3125 - 3130 .

孙诚 , 王志海 . 社会网络中基于神经网络的链路预测方法 [J ] . 数学建模及其应用 , 2017 , 6 ( 4 ): 10 - 17 .

SUN C , WANG Z H . The link prediction algorithms based on neural networks in social networks [J ] . Mathematical Modeling and Its Applications , 2017 , 6 ( 4 ): 10 - 17 .

黄立威 , 李德毅 , 马于涛 , 等 . 一种基于元路径的异质信息网络链路预测模型 [J ] . 计算机学报 , 2014 , 37 ( 4 ): 848 - 858 .

HUANG L W , LI D Y , MA Y T , et al . A meta path-based link prediction model for heterogeneous information networks [J ] . Chinese Journal of Computers , 2014 , 37 ( 4 ): 848 - 858 .

王凯 , 刘树新 , 丁洪涛 , 等 . 基于共同邻居有效性的复杂网络链路预测算法 [J ] . 电子科技大学学报 , 2019 , 48 ( 3 ): 432 - 439 .

WANG K , LIU S X , YU H T , et al . Predicting missing links of complex network via effective common neighbors [J ] . Journal of University of Electronic Science and Technology of China , 2019 , 48 ( 3 ): 432 - 439 .

汤永新 , 齐敬英 . 基于共同邻居的小度节点有利链路预测算法 [J ] . 现代电子技术 , 2021 , 44 ( 5 ): 37 - 40 .

TANG Y X , QI J Y . Algorithm of predicting missing links of small promoted index via common neighbors [J ] . Modern Electronics Technique , 2021 , 44 ( 5 ): 37 - 40 .

JIN W , JUNG J , KANG U . Supervised and extended restart in random walks for ranking and link prediction in networks [J ] . PLoS One , 2019 , 14 ( 3 ): e0213857 .

DONG S L , WU Z G , SHI P , et al . Quantized control of Markov jump nonlinear systems based on fuzzy hidden Markov model [J ] . IEEE Transactions on Cybernetics , 2019 , 49 ( 7 ): 2420 - 2430 .

杨妮亚 . 异质网络中基于元路径的链路预测方法的研究 [D ] . 长春:吉林大学 , 2018 .

YANG N Y . Meta path-based link prediction research for heterogeneous information networks [D ] . Changchun:Jilin University , 2018 .

赵宇红 , 吴昊 . 基于图表示深度学习的异质网络链路预测研究 [J ] . 小型微型计算机系统 ,(2021-12-13)[2022-01-10 ] .

ZHAO Y H , WU H . Link prediction in heterogeneous networks based on deep learning of graph representation [J ] . 小型微型计算机系统 ,(2021-12-13)[2022-01-10 ] .

彭高婧 . 基于PU学习的链接预测方法研究 [D ] . 南京:南京邮电大学 , 2018 .

PENG G J . Research on link prediction method based on PU learning [D ] . Nanjing:Nanjing University of Posts and Telecommunications , 2018 .

崔仕颖 . 基于隐马尔可夫模型的食品安全风险评估方法研究及应用 [D ] . 北京:北京化工大学 , 2020 .

CUI S Y . Research and application of food safety risk assessment algorithm based on hidden Markov model [D ] . Beijing:Beijing University of Chemical Technology , 2020 .

安晓宁 , 王智文 , 张灿龙 , 等 . 基于隐马尔可夫模型的人脸特征标注和识别 [J ] . 广西科技大学学报 , 2020 , 31 ( 2 ): 118 - 125 .

AN X N , WANG Z W , ZHANG C L , et al . Face feature labeling and recognition based on hidden Markov model [J ] . Journal of Guangxi University of Science and Technology , 2020 , 31 ( 2 ): 118 - 125 .

MACQUEEN J , . Some methods for classification and analysis of multivariate observations [C ] // Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability . California:University of California Press , 1967 : 281 - 297 .

丛蓉 , 王秀坤 , 李进军 , 等 . 基于层次和密度聚类分析的航迹关联算法 [J ] . 系统仿真学报 , 2005 , 17 ( 4 ): 841 - 843 .

CONG R , WANG X K , LI J J , et al . Plot-track association algorithm based on hierarchical and density clustering analysis [J ] . Acta Simulata Systematica Sinica , 2005 , 17 ( 4 ): 841 - 843 .

李永森 , 杨善林 , 马溪骏 , 等 . 空间聚类算法中的 K 值优化问题研究 [J ] . 系统仿真学报 , 2006 , 18 ( 3 ): 573 - 576 .

LI Y S , YANG S L , MA X J , et al . Optimization study on K value of spatial clustering [J ] . Journal of System Simulation , 2006 , 18 ( 3 ): 573 - 576 .

KAUFMAN L , ROUSSEEUW P J . Finding groups in data:an introduction to cluster analysis [M ] . Saarland : DBLP , 2009 .

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