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cluTM: Content and Link Integrated Topic Model on Heterogeneous Information Networks

  • Qian Wang
  • Zhaohui Peng
  • Senzhang Wang
  • Philip S. Yu
  • Qingzhong Li
  • Xiaoguang Hong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

Topic model is extensively studied to automatically discover the main themes that pervade a large and unstructured collection of documents. Traditional topic models assume the documents are independent and there are no correlations among them. However, in many real scenarios, a document may be interconnected with other documents and objects, and thus form a text related heterogeneous network, such as the DBLP bibliographic network. It is challenging for traditional topic models to capture the link information associated to diverse types of objects in such a network. To this end, we propose a unified Topic Model cluTM by incorporating both the document content and various links in the text related heterogeneous network. cluTM combines the textual documents and the link structures by the proposed joint matrix factorization on both the text matrix and link matrices. Joint matrix factorization can derive a common latent semantic space shared by multi-typed objects. With the multi-typed objects represented by the common latent features, the semantic information can be therefore largely enhanced simultaneously. Experimental results on DBLP datasets demonstrate the effectiveness of cluTM in both topic mining and multiple objects clustering in text related heterogeneous networks by comparing against state-of-the-art baselines.

Keywords

Information Network Heterogeneous Network Topic Model Latent Dirichlet Allocation Textual Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qian Wang
    • 1
  • Zhaohui Peng
    • 1
  • Senzhang Wang
    • 2
  • Philip S. Yu
    • 3
    • 4
  • Qingzhong Li
    • 1
  • Xiaoguang Hong
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  3. 3.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA
  4. 4.Institute for Data ScienceTsinghua UniversityBeijingChina

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