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Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

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  • Frank Havemann
  • Jochen Gläser
  • Michael Heinz
  • Alexander Struck

Abstract

The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

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  • Frank Havemann & Jochen Gläser & Michael Heinz & Alexander Struck, 2012. "Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-12, March.
    • Handle: RePEc:plo:pone00:0033255
    DOI: 10.1371/journal.pone.0033255
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    References listed on IDEAS

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    1. Michel Zitt & Suzy Ramanana-Rahary & Elise Bassecoulard, 2005. "Relativity of citation performance and excellence measures: From cross-field to cross-scale effects of field-normalisation," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(2), pages 373-401, April.
    2. Frizo Janssens & Wolfgang Glänzel & Bart Moor, 2008. "A hybrid mapping of information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 607-631, June.
    3. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. Wu, Tao & Guo, Yuxiao & Chen, Leiting & Liu, Yanbing, 2016. "Integrated structure investigation in complex networks by label propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 68-80.
    2. Shuo Xu & Junwan Liu & Dongsheng Zhai & Xin An & Zheng Wang & Hongshen Pang, 2018. "Overlapping thematic structures extraction with mixed-membership stochastic blockmodel," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 61-84, October.

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