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Exploring Network Behavior Using Cluster Analysis

Author

Listed:
  • Rong Rong

    () (Department of Economics, Weber State University)

  • Daniel Houser

    () (Interdisciplinary Center for Economic Science and Department of Economics, George Mason University)

Abstract

Innovation occurs in network environments. Identifying the important players in the innovative  process,  namely  “the  innovators†,  is  key to understanding the process of innovation. Doing this requires flexible analysis tools tailored to work well with complex datasets generated within such environments. One such tool, cluster analysis, organizes a large data set into discrete groups based on patterns of similarity. It can be used to discover data patterns in networks without requiring strong ex ante assumptions about the properties of either the data generating process or the environment. This paper reviews key procedures and algorithms related to cluster analysis. Further, it demonstrates how to choose among these methods to identify the characteristics of players in a network experiment where innovation emerges endogenously. Length: 30

Suggested Citation

  • Rong Rong & Daniel Houser, 2014. "Exploring Network Behavior Using Cluster Analysis," Working Papers 1049, George Mason University, Interdisciplinary Center for Economic Science.
  • Handle: RePEc:gms:wpaper:1049
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    More about this item

    Keywords

    cluster analysis; k-means algorithm; innovation; networks; laboratory experiment;

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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