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CHIN: Classification with META-PATH in Heterogeneous Information Networks

  • Jinli Zhang
  • Zongli Jiang
  • Tong Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 942)

Abstract

Most real-word data can be modeled as heterogeneous information networks (HINs), which are composed of multiple types of nodes and links. Classification for objects in HINs is a fundamental problem with broad applications. However, traditional methods cannot involve in heterogeneous information networks. These approaches could not involve the relatedness between objects and various path semantics. In this paper, we proposed a novel framework called CHIN for classification. It utilizes the relevance measurement on objects to iteratively label objects in HINs. As different meta-path performs different accuracy for classification, the proposed framework incorporates the weights of meta-paths. As our experiments show, CHIN generates more accurate classes than the other classification algorithm, but also provides meaningful weights for meta-paths for classification task.

Keywords

Classification Meta-path Heterogeneous information networks 

Notes

Acknowledgments

This work is supported by National Key R&D Program of China (No. 2017YFC08033007), the National Natural Science of Foundation of China (No. 91546111, 91646201) and Basic Research Funding of Beijing University of Technology (No. 040000546318516).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of ComputerBeijing University of TechnologyBeijingChina

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