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Trends in science networks: understanding structures and statistics of scientific networks

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Abstract

The growing of availability of electronic resources over the Internet enables rapid dissemination of the ideas and changes in the trends and the interaction patterns. In this work, we focus on dynamic, evolving social networks which exhibit numerous features that are also of interest to many researchers in non-social fields such as statistical physics, biology, applied mathematics, and computer science. We investigate how a specific research area (high-energy physics) changes over time, by building complex, interlinked citation, publication, and co-publication networks that evolve and expand constantly through the emergence of new papers and authors. Following an interdisciplinary approach, we perform a wide-ranging analysis of the high-energy physics dataset using techniques such as social networks centrality analysis, topological analysis, investigation of power law characteristics, time series analysis of publication and collaboration frequencies, as well as spatiotemporal analysis to discuss relationships among involved countries.

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Notes

  1. Co-publication degree centrality refers to the number of links that start from an author x in the author-to-paper publication network defined in Sect. 3.

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Acknowledgments

The authors would like to thank Niting Qi and Dongyang Teng for their help in extending the dataset studied in this research, and the anonymous reviewers for their feedback and suggestions for improvement. Financial support was provided by the Defense Threat Reduction Agency (DTRA) under grant number HDTRA11010102. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Threat Reduction Agency (DTRA) or the U.S. government.

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  1. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Miray Kas, Kathleen M. Carley & L. Richard Carley

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  1. Miray Kas
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Correspondence to Miray Kas.

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Kas, M., Carley, K.M. & Carley, L.R. Trends in science networks: understanding structures and statistics of scientific networks. Soc. Netw. Anal. Min. 2, 169–187 (2012). https://doi.org/10.1007/s13278-011-0044-6

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