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Identifying High-Status Nodes in Knowledge Networks

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Data Mining for Social Network Data

Part of the book series: Annals of Information Systems ((AOIS,volume 12))

Abstract

The status of a node in a social network plays an important part in determining evolution of the network around it. High-status nodes in knowledge networks are likely to attract more links and influence the use of knowledge by nodes connected directly or indirectly to them. In this study, we model knowledge flow within an innovative organization and contend that it exhibits unique characteristics not incorporated in most social network measures designed to determine node status. Based on the model, we propose the use of a new measure based on team identification and random walks to determine status in knowledge networks. Using data obtained on collaborative patent networks, we find that the new measure performs better than others in identifying high-status inventors.

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Acknowledgments

This research was supported in part by “NanoMap: Mapping Nanotechnology Development,” NSF, Grant #0533749 and the Faculty Development and Research Committee of Towson University. Portions of this chapter were presented in the Hawaii International Conference on System Sciences (HICSS-42) in 2009.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Towson University, Towson, MD, USA

    Siddharth Kaza

  2. Eller College of Management, University of Arizona, 430Z McClelland Hall, E. Helen St. 1130, Tucson, AZ, 85721, USA

    Hsinchun Chen

Authors
  1. Siddharth Kaza
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  2. Hsinchun Chen
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Correspondence to Siddharth Kaza .

Editor information

Editors and Affiliations

  1. , Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark

    Nasrullah Memon

  2. , Dept. Computer Information Systems, Bentley University, Forest St. 175, Waltham, 02452, USA

    Jennifer Jie Xu

  3. , Dept. Computer Science & Engineering, Aalborg University Esbjerg, Niels Bohrs Vej 8, Esbjerg, 6700, Denmark

    David L. Hicks

  4. Eller College of Management, University of Arizona, E. Helen St. 1130, Tucson, 85721, Arizona, USA

    Hsinchun Chen

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Kaza, S., Chen, H. (2010). Identifying High-Status Nodes in Knowledge Networks. In: Memon, N., Xu, J., Hicks, D., Chen, H. (eds) Data Mining for Social Network Data. Annals of Information Systems, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6287-4_6

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