Abbott’s dissertation works focuses on a social network analysis technique called ‘blockmodeling.’ In blockmodeling, individuals within a social network are clustered into groups and then the group relationships are analyzed. Traditionally, blockmodeling has been limited by computing power and the algorithms used for network clustering. This has restricted the size of the networks that could be blockmodeled. To overcome these restrictions, Abbott has implemented optimized versions of traditional algorithms and is currently exploring using machine learning techniques such as artificial neural networks to perform network clustering.
Abbott is also currently writing a methodological book on blockmodeling based on course handouts he wrote for one of his courses and preparing an article comparing blockmodeling algorithms with community detection algorithms.