Title: Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating,
Abstract: Genealogical networks, also known as family trees or population pedigrees,
are commonly studied by genealogists wanting to know about their ancestry, but
they also provide a valuable resource for disciplines such as digital
demography, genetics, and computational social science. These networks are
typically constructed by hand through a very time-consuming process, which
requires comparing large numbers of historical records manually. We develop
computational methods for automatically inferring large-scale genealogical
networks. A comparison with human-constructed networks attests to the accuracy
of the proposed methods. To demonstrate the applicability of the inferred
large-scale genealogical networks, we present a longitudinal analysis on the
mating patterns observed in a network. This analysis shows a consistent
tendency of people choosing a spouse with a similar socioeconomic status, a
phenomenon known as assortative mating. Interestingly, we do not observe this
tendency to consistently decrease (nor increase) over our study period of 150
years.