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Scientometrics

, Volume 117, Issue 1, pp 61–84 | Cite as

Overlapping thematic structures extraction with mixed-membership stochastic blockmodel

  • Shuo Xu
  • Junwan Liu
  • Dongsheng Zhai
  • Xin An
  • Zheng Wang
  • Hongshen Pang
Article
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Abstract

It is increasing important to identify automatically thematic structures from massive scientific literature. The interdisciplinarity enables thematic structures without natural boundaries. In this work, the identification of thematic structures is regarded as an overlapping community detection problem from the large-scale citation-link network. A mixed-membership stochastic blockmodel, armed with stochastic variational inference algorithm, is utilized to detect the overlapping thematic structures. In the meanwhile, in order to enhance readability, each theme is labeled with soft mutual information based method by several topical terms. Extensive experimental results on the astro dataset indicate that mixed-membership stochastic blockmodel primarily uses the local information and allows for the pervasive overlaps, but it favors similar sized themes, which disqualifies this approach from being used to extract the thematic structures from scientific literature. In addition, the thematic structures from the bibliographic coupling network is similar to those from the co-citation network.

Keywords

Overlapping thematic structure Mixed-membership stochastic blockmodel Stochastic variational inference Soft mutual information Cluster labeling 
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Notes

Acknowledgements

The present study is an extended version of an article (Xu et al. 2017) presented at the 16th International Conference on Scientometrics and Informetrics, Wuhan (China), 16–20 October 2017. The clustering results from this work have been deposited with the other astro-dataset results. Our gratitude also goes to the anonymous reviewers and the editor for their valuable comments. This work was supported partially by the Social Science Foundation of Beijing (Grant No. 17GLB074), Science and Technology Project of Guangdong Province (Grant No. 2017A030303065), and National Natural Science Foundation of China (Grant Nos. 71403255 and 71473237).

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© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementBeijing University of TechnologyBeijingPeople’s Republic of China
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunPeople’s Republic of China
  3. 3.School of Economics and ManagementBeijing Forestry UniversityBeijingPeople’s Republic of China
  4. 4.Institute of Scientific and Technical Information of ChinaBeijingPeople’s Republic of China
  5. 5.LibraryShenzhen UniversityShenzhenPeople’s Republic of China

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