A Survey of Heterogeneous Information Network Analysis

被引：740

作者：

Shi, Chuan ^{[1
]}

Li, Yitong ^{[1
]}

Zhang, Jiawei ^{[2
]}

Sun, Yizhou ^{[3
]}

Yu, Philip S. ^{[2
]}

机构：

[1] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China

[2] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA

[3] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA

来源：

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2017年 / 29卷 / 01期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

Heterogeneous information network; data mining; semi-structural data; meta path; K SIMILARITY SEARCH; LINK-PREDICTION; RECOMMENDATION; TRUTH;

D O I：

10.1109/TKDE.2016.2598561

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous information networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous information network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.

引用

页码：17 / 37

页数：21

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