A Tensor-based Markov Chain Model for Heterogeneous Information Network Collective Classification: Extended abstract

Chao Han, Jian Chen, Mingkui Tan, Michael K. Ng, Qingyao Wu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Heterogeneous Information Network(HIN) collective classification aims to classify one type of node, which is associated with multiple types of nodes through multiple types of relations. Previous studies have revealed that exploiting the relative importance of relation types is quite useful for improving node classification performance. We propose a Tensor-based Markov chain (T-Mark) model to improve the nodes classification accuracy by predicting the labels for unlabeled nodes and the importance ranking of relationship types automatically and simultaneously. Specifically, we build two tensor equations according to the HIN structure and content similarities among nodes of both labeled and unlabeled data. Consequently, We solve the semi-supervised T-Mark model by using an iterative process until obtaining two stationary distributions for labels and relation types. Experimental results on several real-world datasets demonstrate the effectiveness of T-Mark.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
PublisherIEEE
Pages3885-3886
Number of pages2
ISBN (Electronic)9798350322279
ISBN (Print)9798350322286
DOIs
Publication statusPublished - 3 Apr 2023
Event39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States
Duration: 3 Apr 20237 Apr 2023
https://icde2023.ics.uci.edu/
https://ieeexplore.ieee.org/xpl/conhome/10184508/proceeding

Publication series

NameProceedings - International Conference on Data Engineering
Volume2023-April
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X

Competition

Competition39th IEEE International Conference on Data Engineering, ICDE 2023
Country/TerritoryUnited States
CityAnaheim
Period3/04/237/04/23
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

User-Defined Keywords

  • Heterogeneous information network
  • Iterative algorithm
  • Markov Chain
  • Node classification
  • Tensor

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