一种基于监督学习的异构网链路预测模型  

Heterogeneous Network Link Prediction Model Based on Supervised Learning

作　　者：黄寿孟[1] HUANG Shou-meng(College of Information and Intelligence Engineering,Sanya University,Sanya,Hainan 572022,China)

机构地区：[1]三亚学院信息与智能工程学院,海南三亚572022

出　　处：《计算机科学》2021年第S02期111-116,共6页Computer Science

基　　金：海南省高等学校科学研究一般项目(Hnky2011-51)。

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。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.

关 键 词：监督学习 异构信息 链路预测 预测模型 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]