Chinese Journal of Sociology ›› 2017, Vol. 37 ›› Issue (2): 1-25.

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Penalized Gaussian Graphic Models and Their Applications in Social Network Measurement

CHEN Huashan   

  1. National Institute of Social Development, CASS
  • Online:2017-03-20 Published:2017-03-20
  • Supported by:

    This study was supported by the Chinese National Social Science Foundation(16BSH013).

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Abstract:

Given the popularity of Internet and new technology,more and more behavioral data recording human interactions has now become available,and attracted the attention of sociological research. Most of the behavioral journal data are of event-action type and are the same data structure as two-mode networks. Two-mode networks are common in social network analysis fields and there are many methods for analyzing two-mode networks. However,unlike the classical two-mode network that is usually a small dataset and suitable for methods such as matrix decomposition,principal component analysis,and other descriptive analysis methods,the underlying network of behavioral data is rather large in scale,with information about time ordered heterogeneous events. Besides,the network members change dynamically,members may join or leave the network. Traditional analytic methods cannot effectively deal with such data. The analysis of such large-scale behavioral data is a huge challenge for social scientists.
Over the past decade, the high dimensional Gaussian graphic model has received a great deal of attention in the research of network structure detection,especially those based on Tibshirani's lasso method of statistical analysis(1996). The success of the lasso based penalized Gaussian graphic model is not only due to its efficiency in high dimensional computation,but also due to its interpretability and ease of extension under further considerations. Hence,the lasso penalized Gaussian graphic model is a rapidly developing field with an overwhelming amount of literature on Biology,Genetics,Neurology,machine learning,etc. However,it hasn't caught the attention from social scientists.
This paper presents an overview of the applications of lasso based penalized Gaussian graphic model for the measurement of network structures with observational behavioral data. The author does not focus on the specific solution algorithms and optimization processes,but rather on the potential substantial contributions of the Gaussian graphic model and its extensions to social science research. This paper derives different hypothesis under theoretical concern and demonstrates with real data examples. Finally,it also briefly summarizes the related models and their R packages,with intent to expand the application of the Gaussian graphic models in social science research.