A Survey of Heterogeneous Information Network Analysis
Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, Philip S. Yu

TL;DR
This survey reviews the analysis of heterogeneous information networks, emphasizing their rich semantic structures and discussing recent developments, challenges, and future research directions in this evolving field.
Contribution
It provides a comprehensive overview of heterogeneous information network analysis, covering basic concepts, recent advances, and future research challenges.
Findings
Heterogeneous networks contain richer structural and semantic information.
Recent methods leverage semantic types for improved data mining.
Future directions include advanced analysis techniques and applications.
Abstract
Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous 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 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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Advanced Clustering Algorithms Research
