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Network analysis is a multidisciplinary research method that is quickly becoming a popular and exciting field. Though some statistical programs possess sophisticated packages for analyzing networks, similar capabilities have yet to be made available in Stata. In an effort to motivate the use of Stata for network analysis, I designed in Mata the Stata graph library (SGL), which consists of algorithms that construct matrix representations of networks, compute centrality measures, calculate clustering coefficients, and solve maximum-flow problems. The SGL is designed for both directed and undirected one-mode networks containing edges that are either unweighted or weighted with positive values. Performance tests conducted between C++ and Stata graph library implementations indicate gross inefficiencies in current SGL routines, making the SGL impractical for large networks. The obstacles are, however, welcome challenges in the effort to spread the use of Stata for analyzing networks. Future developments will focus toward addressing computational time complexities and integrating additional capabilities into the SGL.

Keywords: netsis ; netsummarize ; centrality ; clustering ; network analysis

Subject(s): Research Methods/ Statistical Methods

Issue Date: 2012

Publication Type: Journal Article

DOI and Other Identifiers: st0248 (Other)

PURL Identifier: http://purl.umn.edu/202126 Published in: Stata Journal, Volume 12, Number 1 Page range: 94-129

Total Pages: 38

Record appears in: Stata Journal





Autor: Miura, Hirotaka

Fuente: http://ageconsearch.umn.edu/record/202126?ln=en







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