Survey of link prediction method in heterogeneous information network

被引：0
作者：
Cao, Jiaping [1 ]
Li, Jichao [1 ]
Jiang, Jiang [1 ]
机构：
[1] College of Systems Engineering, National University of Defense Technology, Changsha
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 08期
关键词：
heterogeneous information network; link prediction; meta-path; supervised learning;
D O I：
10.12305/j.issn.1001-506X.2024.08.22
中图分类号：
学科分类号：
摘要：
Link prediction is the prediction of unknown or future links based on known information in the network, and is one of the research hotspots in the field of data mining. Heterogeneous information network can accurately portray the semantic information from data and improve the efficiency of downstream data mining tasks. Therefore, link prediction method on heterogeneous information network needs to take into account the topological characteristics and semantic characteristics of the network, which brings new challenges to the link prediction task. On the basis of previous research, this paper systematically sorts out the link prediction methods on heterogeneous information network in the past decade. Firstly, the concepts of heterogeneous information network and link prediction are introduced. Secondly, the link prediction methods in heterogeneous information network is classified, and the link prediction methods in different types of heterogeneous information network are summarized. Further more, the typical methods are introduced in detail. Then, the application of link prediction methods in heterogeneous information network are sorted. Finally, the problems that need to be addressed in further research in this field and potential future resarch directions are summaried. © 2024 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：2747 / 2759
页数：12
相关论文
共 175 条
[31]  
ZHANG Y G, ZHANG C S, ZHANG D., Distance metric learning by knowledge embedding, Pattern Recognition, 37, 1, pp. 161-163, (2004)
[32]  
HAO Y C, ZHANG Y Z, LIU K, Et al., An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge, Proc. of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 221-231, (2017)
[33]  
GUPTA S, KUMAR P., Community detection in heterogenous networks using incremental seed expansion, Proc. of the 3rd International Conference on Data Science and Engineering, pp. 184-188, (2016)
[34]  
REN X, LIU J L, YU X, Et al., ClusCite: effective citation re-commendation by information network-based clustering, Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 821-830, (2014)
[35]  
ABABIO 1 B, CHEN J X, CHEN Y, Et al., Link prediction based on heuristics and graph attention, Proc. of the 8th IEEE International Conference on Big Data, pp. 5428-5434, (2020)
[36]  
LI J W, LIU Y, LUO X L., Semi-supervised learning methods, Chinese Journal of Computers, 38, 8, pp. 1592-1617, (2015)
[37]  
SUN Y Z, BARBER R, GUPTA M, Et al., Co-author relationship prediction in heterogeneous bibliographic networks, Proc. of the International Conference on Advances in Social Networks Analysis and Mining, pp. 121-128, (2011)
[38]  
ZHANG J W, YU P S, LYU Y H, Et al., Organizational chart inference, Proc. of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1435-1444, (2015)
[39]  
SHI C, ZHOU C, KONG X N, Et al., HeteRecom: a semantic-based recommendation system in heterogeneous networks, Proc. of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1552-1555, (2012)
[40]  
SHI C, ZHANG Z Q, LUO P, Et al., Semantic path based personalized recommendation on weighted heterogeneous information networks, Proc. of the 24th ACM International on Conference on Information and Knowledge Management, pp. 453-462, (2015)
12345678910