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Overlaying communities and topics: an analysis on publication networks

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Abstract

Two layers of enriched information are constructed for communities: a paper-to-paper network based on shared author relations and a paper-to-paper network based on shared word relations. k-means and VOSviewer, a modularity-based clustering technique, are used to identify publication clusters in the two networks. Results show that a few research topics such as webometrics, bibliometric laws, and language processing, form their own research community; while other research topics contain different research communities, which may be caused by physical distance.

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Notes

  1. See data and interactive visualizations at http://info.slis.indiana.edu/~eyan/papers/cluster/.

  2. http://www.isiwebofknowledge.com/.

  3. http://www.vosviewer.com/.

  4. http://info.slis.indiana.edu/~eyan/papers/cluster/cluster_results.xlsx.

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Acknowledgments

The authors would like to thank Ludo Waltman, Nees van Eck, and Ed Noyons for introducing VOSviewer and their comments to the idea of this paper.

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Authors and Affiliations

  1. School of Library and Information Science, Indiana University, Bloomington, IL, USA

    Erjia Yan, Ying Ding & Elin K. Jacob

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  1. Erjia Yan
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  2. Ying Ding
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  3. Elin K. Jacob
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Correspondence to Erjia Yan.

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Yan, E., Ding, Y. & Jacob, E.K. Overlaying communities and topics: an analysis on publication networks. Scientometrics 90, 499–513 (2012). https://doi.org/10.1007/s11192-011-0531-6

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