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

TL;DR

This paper introduces computational methods to automatically infer large-scale genealogical networks, enabling long-term analysis of mating patterns and revealing persistent socioeconomic assortative mating over 150 years.

Contribution

The paper presents novel algorithms for automatic construction of genealogical networks and validates their accuracy against human-constructed networks.

Findings

Accurate large-scale genealogical networks inferred computationally.

Persistent socioeconomic assortative mating observed over 150 years.

No significant change in assortative mating trend over the study period.

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.