Exploring Network Behavior Using Cluster Analysis
Rong Rong () and
Daniel Houser ()
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Rong Rong: Department of Economics, Weber State University
No 1049, Working Papers from George Mason University, Interdisciplinary Center for Economic Science
Innovation occurs in network environments. Identifying the important players in the innovative Â process, Â namely Â â€œthe Â innovatorsâ€ , Â is Â 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
Keywords: cluster analysis; k-means algorithm; innovation; networks; laboratory experiment (search for similar items in EconPapers)
JEL-codes: C46 C81 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-cse, nep-gth, nep-ino, nep-knm and nep-sbm
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