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Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis

Author

Listed:
  • Theresa Velden

    (University of Michigan School of Information
    Technical University Berlin)

  • Shiyan Yan

    (University of Michigan School of Information)

  • Carl Lagoze

    (University of Michigan School of Information)

Abstract

In this paper we use the information theoretic Infomap algorithm (Rosvall and Bergstrom in Proc Natl Acad Sci 105(4):1118–1123, 2008) iteratively in order to cluster the direct citation network of the Astro Data Set (publications in 59 astrophysical journals between 2003 and 2010.) We obtain 22 clusters of documents from the giant component of the network that we interpret as constituting ‘topics’ in the field of astrophysics. Upon investigation of the content of the topics we find a grouping of topics by shared features of their ‘journal signature’, that is the journals that are most characteristic for a topic due to their popularity and distinctiveness. These groups of topics match sub disciplines within the field. We generate a cognitive map of the field using a topic affinity network that shows what topics are disproportionally well connected (by citations) to other topics. The topology of the topic affinity network highlights a high-level organization of the field by sub-discipline and observational distance of the research object from Earth.

Suggested Citation

  • Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2299-9
    DOI: 10.1007/s11192-017-2299-9
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    References listed on IDEAS

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    14. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
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    2. Hric, Darko & Kaski, Kimmo & Kivelä, Mikko, 2018. "Stochastic block model reveals maps of citation patterns and their evolution in time," Journal of Informetrics, Elsevier, vol. 12(3), pages 757-783.
    3. Paul Donner, 2021. "Validation of the Astro dataset clustering solutions with external data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1619-1645, February.
    4. Peter Sjögårde & Per Ahlgren & Ludo Waltman, 2021. "Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 853-869, July.
    5. García-Lillo, Francisco & Seva-Larrosa, Pedro & Sánchez-García, Eduardo, 2023. "What is going on in entrepreneurship research? A bibliometric and SNA analysis," Journal of Business Research, Elsevier, vol. 158(C).
    6. 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.
    7. 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.
    8. Gerson Pech & Catarina Delgado & Silvio Paolo Sorella, 2022. "Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures: An application in Physics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(11), pages 1513-1528, November.
    9. Jean-Charles Lamirel & Younes Gueddari & Yuqi Wang & Pascal Cuxac & Anthony Perez & Nicolas Dugué, 2021. "Analysis of the dynamics and influence of the research work of Prof. Liu Zeyuan in China featuring a new hybrid approach combining community detection with topic tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6273-6300, July.
    10. Wang, Feifei & Jia, Chenran & Wang, Xiaohan & Liu, Junwan & Xu, Shuo & Liu, Yang & Yang, Chenyuyan, 2019. "Exploring all-author tripartite citation networks: A case study of gene editing," Journal of Informetrics, Elsevier, vol. 13(3), pages 856-873.

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