FIGURE D-2-5 Disciplinary heterogeneity of the NSRN.

Social Network of Network Science Researchers: Topics for Further Study

  • Increase our understanding of the interplay of affiliation, thematic, and social interrelations among today’s network science researchers. Invite key network science researchers to identify and label the main research groups key shown in Figure D-2-2.

  • Bibliometric analysis of networkscience publications, patents, and funding data. The InfoVis Laboratory at Indiana University is developing the sociotechnical infrastructure to analyze the structure and evolution of scientific disciplines and all of science on a large scale.7 Major publication, patent, and grant databases (covering mostly U.S. research) are available, as are scalable algorithms and compute power. A detailed, objective analysis of scholarly data would complement the self-reported, subjective data and its analysis reported here.

  • Development of an online portal that tracks and communicates the evolution of network science research and results. Geospatial and semantic maps of network science researchers and publications presented here and proposed in Shiffrin and Börner (2004) can be made available online as a unique interface to data sets, publications, and expertise related to network science research. Researchers interested in being “on the map” should be given the option to submit data about their publications, collaborators, etc. The incentives for researchers to contribute high-quality data can be further increased by using this online map to make funding decisions much as PI’s resumes are used today. Assuming that a comprehensive set of high-quality data can be acquired on a continuous basis, an interactive, continuously evolving, weather-forecast-like map of network science research can be made available to grant agencies, researchers, practitioners, and society at large.

NOTE: The authors would like to thank Will E. Leland for compiling the data set used in this study and for insightful feedback on previous results. This work is supported by a National Science Foundation CAREER Grant under IIS-0238261.

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Shiffrin, R.M., and K. Börner, eds. 2004. Mapping knowledge domains. Proceedings of the National Academy of Sciences of the United States 101 (Suppl. 1).

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Batagelj, V., and A. Mrvar. 1997. Pajek: Program package for large network analysis. Available at http://vlado.fmf.uni-lj.si/pub/networks/pajek/.

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Freeman, L.C. 1997. A set of measuring centrality based on betweenness. Sociometry 40:35–41.

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Brandes, U. 2001. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25(1): 163–177.

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Davidson, G.S., B. Hendrickson, D.K. Johnson, C.E. Meyers, and B.N. Wylie. 1998. Knowledge mining with VxInsight: Discovery through interaction. Journal of Intelligent Information Systems 11(3): 259–285.

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Available at http://iv.slis.indiana.edu.



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