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

Velden, Theresa; Yan, Shiyan; Lagoze, Carl

doi:10.1007/s11192-017-2299-9

Cited by 26 publications

(19 citation statements)

References 27 publications

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“…[Network located at one end, the domain of Astrophysics in the middle, and the domains of Solar Physics, Planetary Science, and Space Science located at the other end. This structure suggests an organization of the field from objects at large scales of space-time and larger distance from earth to smaller objects and closer distance to earth, as discussed in Velden et al (2017). Solutions eb and sr can be seen as exposing variants of this pattern.…”

Section: Degree Of Similarity Between Solutionsmentioning

confidence: 86%

“…E.g. from the four solutions offered by Van Eck and Waltman (2017) with 22, 42, 115, 434 clusters respectively, we chose the solution with 22 clusters to coincide with the 22 clusters delivered by Velden et al (2017). The two sets of solutions offered by Glänzel and Thijs (2017) were all of very low resolution, hence we chose the solutions with highest resolution: a solution with 13 instead of the alternative solution with 6 clusters, and a solution with 11 instead of the alternative with 5 clusters.…”

Section: Overview On Topic Extraction Approachesmentioning

confidence: 99%

“…The method shows how the document clusters extracted relate to one another based on direct citation links between documents. In the calculation of link strengths between document clusters only the surplus of citations relative to a random null model (based on cluster sizes) are considered and mapped in order to reduce the 'cluttering' of the visualization from a pervasive background of connectivity within the scientific literature (see Velden et al, 2017;Velden and Lagoze, 2013, for details of the method.) See appendix A.1 for download information for the corresponding data files.…”

Section: Lexical Fingerprintmentioning

confidence: 99%

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Comparison of topic extraction approaches and their results

et al. 2017

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This is a preprint of an article accepted to be published in a special issue of Scientometrics: Gläser, J., Scharnhorst, A. and Glänzel, W. (eds), Same data -different results? Towards a comparative approach to the identification of thematic structures in science. This is the last paper in the Synthesis section of the special issue on 'Same Data, Different Results'. We first provide a framework of how to describe and distinguish approaches to topic extraction from bibliographic data of scientific publications. We then compare solutions delivered by the different topic extraction approaches in this special issue, and explore where they agree and differ. This is achieved without reference to a ground truth, since we have to assume the existence of multiple, equally important, valid perspectives and want to avoid bias through the adoption of an ad-hoc yardstick. Instead, we apply different ways to quantitatively and visually compare solutions to explore their commonalities and differences and develop hypotheses about the origin of these differences. We conclude with a discussion of future work needed to develop methods for comparison and validation of topic extraction results, and express our concern about the lack of access to non-proprietary benchmark data sets to support method development in the field of scientometrics.

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Section: Degree Of Similarity Between Solutionsmentioning

confidence: 86%

Section: Overview On Topic Extraction Approachesmentioning

confidence: 99%

Section: Lexical Fingerprintmentioning

confidence: 99%

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Comparison of topic extraction approaches and their results

et al. 2017

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“…Velden applied the Infomap algorithm (as described by Velden et al 2017b) to the 2010 network of papers and their cited sources and provided the results for use by Havemann et al (2017). Havemann et al (2017) applied their algorithm to the 2003-2010 direct citation network that was used by Velden et al (2017b) and Van Eck and Waltman (2017). Wang and Koopman (2017) additionally used the Louvain algorithm with their data model.…”

Section: A Comparative Approach To Unresolved Issuesmentioning

confidence: 99%

“…The comparison of their solutions to each other and to the other solutions provided in this volume suggest that clustering solutions depend more strongly on the data models than on the algorithms used. Velden et al (2017b) construct the direct citation network of the Astro-dataset, to which they apply the Infomap algorithm. Since this algorithm produces nearly 2000 clusters, it is applied a second time to these clusters and thus leads to a solution consisting of 21 clusters.…”

Section: Introducing Contributions To the Special Issuementioning

confidence: 99%

Same data—different results? Towards a comparative approach to the identification of thematic structures in science

2017

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Science studies are persistently challenged by the elusive structures of their subject matter, be it scientific knowledge or the various collectivities of researchers engaged with its production. Bibliometrics has responded by developing a strong and growing structural bibliometrics, which is concerned with delineating fields and identifying thematic structures. In the course of these developments, a concern emerged and is steadily growing. Do the sets of publications, authors or institutions we identify and visualise with our methods indeed represent thematic structures? To what extent are results of topic identification exercises determined by properties of knowledge structures, and to what extent are they determined by the approaches we use? Do we produce more than artefacts? These questions triggered the collective process of comparative topic identification reported in this special issue. The introduction traces the history of bibliometric approaches to topic identification, identifies the major challenges involved in these exercises, and introduces the contributions to the special issue.

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Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches

Sjögårde

Ahlgren

Waltman

2021

Asso for Info Science & Tech

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Algorithmic classifications of research publications can be used to study many different aspects of the science system, such as the organization of science into fields, the growth of fields, interdisciplinarity, and emerging topics. How to label the classes in these classifications is a problem that has not been thoroughly addressed in the literature. In this study, we evaluate different approaches to label the classes in algorithmically constructed classifications of research publications. We focus on two important choices: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms. To evaluate the different choices, we created two baselines: one based on the Medical Subject Headings in MEDLINE and another based on the Science-Metrix journal classification. We tested to what extent different approaches yield the desired labels for the classes in the two baselines. Based on our results, we recommend extracting terms from titles and keywords to label classes at high levels of granularity (e.g., topics). At low levels of granularity (e.g., disciplines) we recommend extracting terms from journal names and author addresses. We recommend the use of a new approach, term frequency to specificity ratio, to calculate the relevance of terms.

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

Cited by 26 publications

References 27 publications

Comparison of topic extraction approaches and their results

Comparison of topic extraction approaches and their results

Same data—different results? Towards a comparative approach to the identification of thematic structures in science

Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches

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