Belief Network Timeseries Analaysis

Last year I came across a working paper for AJS on Belief Network Analysis by Andrei Boutyline [1]. The paper looks at American National Election Survey data and examines two theories for the process of political opinion formation: Lakoff’s Theory of Moral Politics and Campbell’s Theory of Political Identity. This project, in collaboration with Sujaya Maiyya, was focused on extending BNA to the American National Election Survey timeseries data to test some of the claims Andrei made in response to an investigation from Delia Baldassarri using Relational Class Analysis [2]. The original work was performed by analyzing survey data from the year 2000, but we argue that no claims can be made about this process unless we make a longitudinal investigation.

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NetworkXTimeseries: A Python Library for Network Timeseries Data Structures

Several of my projects over the last few months have leaned in the direction of longitudinal studies. The question every sociologist asks is “how did we get here?”, so it makes sense that one would like to explore how things have been changing before now. My conclusion is that if networks provide meaningful investigation into the types of questions we are trying to answer, then we need to understand how these networks change over time.

This Python library is useful because it allows one to easily transition from a traditional networkx  object to simple time series dataframe representations in Pandas and Numpy. I’ve already used it in several projects and I hope you can use it too!

networkxtimeseries Python Library GitHub

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Weighted Correlation Network Analysis Python Library

Along with recent shifts in the Sociology of Culture towards relational techniques is the use of the correlation network. Instead of examining the answers to survey responses themselves, these approaches look at relationships between questions and try to take meaning from structural properties of the whole. I also used some of these techniques for the GHTC 2016 conference [1] with Lee exploring USAID data from Guatemala. The results appear in our paper [1], but the true inspiration comes from the work on statistical methods for gene co-expression [2]. One particularly exciting work in sociology [3] also tries to explain the structure of political beliefs using these networks.

I plan to do further work using these techniques, so I created a python library for anyone interested.

WCNA Python Library GitHub

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