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Genealogical networks, also known as family trees or population pedigrees,are commonly studied by genealogists wanting to know about their ancestry, butthey also provide a valuable resource for disciplines such as digitaldemography, genetics, and computational social science. These networks aretypically constructed by hand through a very time-consuming process, whichrequires comparing large numbers of historical records manually. We developcomputational methods for automatically inferring large-scale genealogicalnetworks. A comparison with human-constructed networks attests to the accuracyof the proposed methods. To demonstrate the applicability of the inferredlarge-scale genealogical networks, we present a longitudinal analysis on themating patterns observed in a network. This analysis shows a consistenttendency of people choosing a spouse with a similar socioeconomic status, aphenomenon known as assortative mating. Interestingly, we do not observe thistendency to consistently decrease (nor increase) over our study period of 150years.

Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating