Network-based document clustering using external ranking loss for network embedding

Yeo Chan Yoon, Hyung Kuen Gee, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Network-based document clustering involves forming clusters of documents based on their significance and relationship strength. This approach can be used with various types of metadata that express the significance of the documents and the relationships among them. In this study, we defined a probabilistic network graph for fine-grained document clustering and developed a probabilistic generative model and calculation method. Furthermore, a novel neural-network-based network embedding learning method was devised that considers the significance of a document based on its rankings with external measures, such as the download counts of relevant files, and reflects the relationship strength between the documents. By considering the significance of a document, reputative documents of clusters can be centralized and shown as representative documents for tasks such as data analysis and data representation. During evaluation tests, the proposed ranking-based network-embedding method performs significantly better on various algorithms, such as the k-means algorithm and common word/phrase-based clustering methods, than the existing network embedding approaches.

Original languageEnglish
Article number8878093
Pages (from-to)155412-155423
Number of pages12
JournalIEEE Access
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported by the Institute for Information and Communications Technology Promotion grant funded by the Korean Government (Digital Content In-House Research and Development under Grant 2016-0-00010-003.

Publisher Copyright:
© 2013 IEEE.


  • Clustering algorithms
  • artificial neural networks

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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