计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 111-116.doi: 10.11896/jsjkx.210300030

• 智能计算 • 上一篇    下一篇

一种基于监督学习的异构网链路预测模型

黄寿孟   

  1. 三亚学院信息与智能工程学院 海南 三亚572022
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 黄寿孟(huang123888@126.com)
  • 基金资助:
    海南省高等学校科学研究一般项目(Hnky2011-51)

Heterogeneous Network Link Prediction Model Based on Supervised Learning

HUANG Shou-meng   

  1. College of Information and Intelligence Engineering,Sanya University,Sanya,Hainan 572022,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:HUANG Shou-meng,born in 1975,master,associate professor.His main research interests inclue information technology and information security.
  • Supported by:
    Education Department of Hainan Province(Hnky2021-51).

摘要: 传统的异构网链路预测研究有基于元路径监督学习的PathPredict算法与MPBP算法,但它们并不能充分利用异构网提供的丰富信息来进行链路预测。在原有传统监督学习算法的基础上,首先为了增加链路熵和时间动态信息而设计了HLE-T算法,然后通过链路强弱关系的数值分段构建多分类问题的监督学习算法MSLP链路预测模型,最后在4个稠密度不同的数据集下完成了实验测试。实验结果表明,MSLP链路预测模型一定程度上提升了异构网中的链路预测性能,对未来链路预测研究具有一定的借鉴意义。

关键词: 监督学习, 链路预测, 异构信息, 预测模型

Abstract: The research on traditional heterogeneous network link prediction has path-predicted algorithm and MPBP(meta-path feature-based backpPropagation neural network model) algorithm based on the metapath supervised learning.However,they can't make full use of the rich information provided by heterogeneous network to make link prediction.Based on the traditional supervised learning algorithm,this paper first designs the HLE-T(heterogeneous link entropy with time) algorithm in order to increase the link entropy and time dynamic information.Moreover,it constructs the MSLP(modified supervised link prediction)model of the Supervised learning algorithm with the multi-classification problem by the numerical segment of the link strength and weak relationship,and finally completes the experimental test under four data sets with different density.The experimental results show that the MSLP model improves the link prediction performance in heterogeneous network to some extent,and has some reference significance for the future link prediction research.

Key words: Heterogeneous information, Link prediction, Predictive model, Supervised learning

中图分类号: 

  • TP309
[1]SUN Y,HAN J.Mining Heterogeneous Information Networks:A Structural Analysis Approach[J].ACM SIGKDD Explorations Newsletter,2013,14(2):20-28.
[2]HU W,LI J,CHENG J,et al.Security Monitoring of Heterogeneous Networks for Big Data Based on Distributed Association Algorithm[J].Computer Communications,2020,152:206-214.
[3]KOVÁCS I A,LUCK K,SPIROHN K,et al.Network-basedPrediction of Protein Interactions[J].Nature Communications,2019,10(1):1-8.
[4]DAUD A,AHMAD M,MALIK M S I,et al.Using Machine Learning Techniques for Rising Star Prediction in Co-author Network[J].Scientometrics,2015,102(2):1687-1711.
[5]SHI C,LI Y,ZHANG J,et al.A Survey of Heterogeneous Information Network Analysis[J].IEEE Transactions on Knowledge and Data Engineering,2016,29(1):17-37.
[6]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.
[7]JIANG L,YANG C C.User Recommendation in Healthcare Social Media by Assessing User imilarity in Heterogeneous Network[J].Artificial Intelligence in Medicine,2017,81(9):63-77.
[8]ZHANG F,WANG M,XI J,et al.A Novel Heterogeneous Network-based Method for Drug Response Prediction in Cancer Cell Lines[J].Scientific Reports,2018,8(1):355-367.
[9]LIANG W,LI X,HE X,et al.Supervised Ranking Framework for Relationship Prediction in Heterogeneous Information Networks[J].Applied Intelligence,2018,48(5):1111-1127.
[10]LI J,ZHAO D,GE B F,et al.A Link Prediction Method forHeterogeneous Networks Based on BP Neural Network[J].Physica A-Statistical Mechanics and Its Applications,2018,495(1):1-16.
[11]PENG Y C.Research on Link Prediction in Heterogeneous Information Networks[D].Harbin:Harbin Institute of Technology,2020.
[12]LAI J,SHENG H L.Research on Link Prediction Performance of Complex Networks Based on Clustering Analysis[J].Computing Technology and Automation,2019(4):144-150.
[13]WANG H ,LE Z C,GONG X,et al.Link Prediction of Complex Networks is Analyzed from the Perspective of Informatics[J].Journal of Chinese Computer Systems,2020,41(2):316-326.
[14]BAI H,MA Y L,BI Y,et al.A Complicated Network Link Prediction Algorithm Based on Local Similarity of Nodes[J].Computer Applications and Software,2020,37(5):298-301.
[15]LIU S X,LI X,CHEN H C,et al.Link prediction method based on matching degree of resource transmission for complex network[J].Journal on Communications,2020,41(6):70-79.
[16]QI F P,WANG T,FU Z Q.Link prediction in complex networks based on mutual information[J].Journal of University of Science and Technology of China,2020,50(1):57-63.
[17]REVELLE M,DOMENICONI C,SWEENEY M,et al.Finding Community Topics and Membership in Graphs[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.2015:625-640.
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