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Memetic search for overlapping topics based on a local evaluation of link communities

Author

Listed:
  • Frank Havemann

    (Humboldt-Universität zu Berlin)

  • Jochen Gläser

    (TU Berlin)

  • Michael Heinz

    (Humboldt-Universität zu Berlin)

Abstract

In spite of recent advances in field delineation methods, bibliometricians still don’t know the extent to which their topic detection algorithms reconstruct ‘ground truths’, i.e., thematic structures in the scientific literature. In this paper, we demonstrate a new approach to the delineation of thematic structures that attempts to match the algorithm to theoretically derived and empirically observed properties all thematic structures have in common. We cluster citation links rather than publication nodes, use predominantly local information and search for communities of links starting from seed subgraphs in order to allow for pervasive overlaps of topics. We evaluate sets of links with a new cost function and assume that local minima in the cost landscape correspond to link communities. Because this cost landscape has many local minima we define a valid community as the community with the lowest minimum within a certain range. Since finding all valid communities is impossible for large networks, we designed a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches. We apply our approach to a network of about 15,000 Astronomy and Astrophysics papers published 2010 and their cited sources, and to a network of about 100,000 Astronomy and Astrophysics papers (published 2003–2010) which are linked through direct citations.

Suggested Citation

  • Frank Havemann & Jochen Gläser & Michael Heinz, 2017. "Memetic search for overlapping topics based on a local evaluation of link communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1089-1118, May.
  • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2302-5
    DOI: 10.1007/s11192-017-2302-5
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    2. Matthias Held & Grit Laudel & Jochen Gläser, 2021. "Challenges to the validity of topic reconstruction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4511-4536, May.
    3. A. V. Chumachenko & B. G. Kreminskyi & Iu. L. Mosenkis & A. I. Yakimenko, 2020. "Dynamics of topic formation and quantitative analysis of hot trends in physical science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 739-753, October.
    4. 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.
    5. A. V. Chumachenko & B. G. Kreminskyi & Iu. L. Mosenkis & A. I. Yakimenko, 0. "Dynamics of topic formation and quantitative analysis of hot trends in physical science," Scientometrics, Springer;Akadémiai Kiadó, vol. 0, pages 1-15.
    6. Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
    7. Paul Donner, 2021. "Validation of the Astro dataset clustering solutions with external data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1619-1645, February.

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