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

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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.

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Notes

  1. Technically a similarity matrix can always be interpreted as an adjacency matrix that specifies for each pair of nodes in a network the strength of their connection. There exists a practical difference however between cases where the data model operationalizes the relationship between entities by measuring some direct interaction, e.g. a citation from one document to another, versus cases where the relationship between entities is operationalized as a similarity, e.g. the similarity in how two documents are citing or being cited by all other documents in the data set. In the former case, the network will typically be sparse, whereas in the latter case the network is typically dense and commonly some threshold is applied to suppress weak links between nodes to make calculations on the network easier.

  2. Today, the hierarchical generalization of the map equation introduced in (Rosvall and Bergstrom 2011) makes it possible to cluster networks hierarchically with nested clusters in a principled way, see http://www.mapequation.org/code.html.

  3. Since we designed our workflow, alternatives to completely disregarding the directionality have become available, such as to limit the number of steps and perform unrecorded teleportation that does not influence the clustering (Lambiotte and Rosvall 2012). According to Rosvall (private communication), using the flag—undirdir in the code provided at http://www.mapequation.org/code.html triggers a two-mode dynamics that assumes undirected links for calculating flows, but directed links when minimizing the code length. It has been used e.g. in (Mirshahvalad et al. 2012; West et al. 2016).

  4. A more systematic and comprehensive method for determining variation in the form of a significance analysis is described in Rosvall and Bergstrom (2010).

  5. One of the authors was trained in gravitational physics and worked several years as managing editor of the scientific review journal Living Reviews in Relativity.

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Acknowledgements

We gratefully acknowledge funding from SMA 1258891 EAGER: Collaborative Research: Scientific Collaboration in Time, as well as a travel Grant by the intergovernmental framework for European Cooperation in Science and Technology (COST, Action: TD1210). We further thank Martin Rosvall for comments on pertinent new developments of the Infomap algorithm.

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  1. Theresa Velden

    Present address: ZTG, Technical University Berlin, Hardenbergstr. 16-18, 10623, Berlin, Germany

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  1. University of Michigan School of Information, 109 S. State Street, Ann Arbor, MI, USA

    Theresa Velden, Shiyan Yan & Carl Lagoze

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Correspondence to Theresa Velden.

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Velden, T., Yan, S. & Lagoze, C. Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis. Scientometrics 111, 1033–1051 (2017). https://doi.org/10.1007/s11192-017-2299-9

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