A survey of heterogeneous information network analysis

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 th...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2017-01, Vol.29 (1), p.17-37
Main Authors: Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, Yu, Philip S.
Format: Article
Language:English
Subjects:
Analytical models
Conferences
Data mining
Data models
Heterogeneous information network
meta path
Network analysis
Publishing
Semantics
semi-structural data
Structural analysis
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2016.2598561