Heterogeneous Network Link Prediction Model Based on Supervised Learning

Abstract

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

CLC Number:

TP309

HUANG Shou-meng. Heterogeneous Network Link Prediction Model Based on Supervised Learning[J].Computer Science, 2021, 48(11A): 111-116.

References

[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.

Related Articles 15

[1]	SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[2]	HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82.
[3]	LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi. Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network [J]. Computer Science, 2022, 49(9): 92-100.
[4]	WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[5]	DU Hang-yuan, LI Duo, WANG Wen-jian. Method for Abnormal Users Detection Oriented to E-commerce Network [J]. Computer Science, 2022, 49(7): 170-178.
[6]	HOU Xia-ye, CHEN Hai-yan, ZHANG Bing, YUAN Li-gang, JIA Yi-zhen. Active Metric Learning Based on Support Vector Machines [J]. Computer Science, 2022, 49(6A): 113-118.
[7]	PANG Xing-long, ZHU Guo-sheng. Survey of Network Traffic Analysis Based on Semi Supervised Learning [J]. Computer Science, 2022, 49(6A): 544-554.
[8]	WANG Yu-fei, CHEN Wen. Tri-training Algorithm Based on DECORATE Ensemble Learning and Credibility Assessment [J]. Computer Science, 2022, 49(6): 127-133.
[9]	ZHANG Wen-xuan, WU Qin. Fine-grained Image Classification Based on Multi-branch Attention-augmentation [J]. Computer Science, 2022, 49(5): 105-112.
[10]	LI Yong, WU Jing-peng, ZHANG Zhong-ying, ZHANG Qiang. Link Prediction for Node Featureless Networks Based on Faster Attention Mechanism [J]. Computer Science, 2022, 49(4): 43-48.
[11]	XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo. Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [J]. Computer Science, 2022, 49(3): 288-293.
[12]	ZHAO Xue-lei, JI Xin-sheng, LIU Shu-xin, LI Ying-le, LI Hai-tao. Link Prediction Method for Directed Networks Based on Path Connection Strength [J]. Computer Science, 2022, 49(2): 216-222.
[13]	HOU Hong-xu, SUN Shuo, WU Nier. Survey of Mongolian-Chinese Neural Machine Translation [J]. Computer Science, 2022, 49(1): 31-40.
[14]	JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139.
[15]	ZHENG Su-su, GUAN Dong-hai, YUAN Wei-wei. Heterogeneous Information Network Embedding with Incomplete Multi-view Fusion [J]. Computer Science, 2021, 48(9): 68-76.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!