A Survey of Heterogeneous Information Network Analysis

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 its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.