IEEE Transactions on Knowledge and Data Engineering· 2016Q1· Review
A survey of heterogeneous information network analysis
- 1,115citations
- Q1SCImago
- 2016year
Short summary
This survey introduces heterogeneous information networks (HINs), which model real-world systems with diverse interacting components, unlike simpler homogeneous networks.
AI-generated from the title and abstract; the full text is not read.
Key points
- Real-world systems are often composed of multi-typed, interacting components.
- Heterogeneous information networks (HINs) model these systems by distinguishing object and link types.
- HINs offer richer structure and semantic information compared to homogeneous networks.
- The survey covers basic concepts, data mining applications, advanced topics, and future research in HIN analysis.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. IEEE Transactions on Knowledge and Data Engineering, 2016 · DOI ↗
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Field: Statistical and Nonlinear Physics
Statistical and Nonlinear PhysicsPhysics and Astronomy