Extracting hierarchical features of cultural variation using network-based clustering

Xiran Liu, Noah A. Rosenberg*, Gili Greenbaum

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


High-dimensional datasets on cultural characters contribute to uncovering insights about factors that influence cultural evolution. Because cultural variation in part reflects descent processes with a hierarchical structure - including the descent of populations and vertical transmission of cultural traits - methods designed for hierarchically structured data have potential to find applications in the analysis of cultural variation. We adapt a network-based hierarchical clustering method for use in analysing cultural variation. Given a set of entities, the method constructs a similarity network, hierarchically depicting community structure among them. We illustrate the approach using four datasets: pronunciation variation in the US mid-Atlantic region, folklore variation in worldwide cultures, phonemic variation across worldwide languages and temporal variation in first names in the US. In these examples, the method provides insights into processes that affect cultural variation, uncovering geographic and other influences on observed patterns and cultural characters that make important contributions to them.

Original languageAmerican English
Article numbere18
JournalEvolutionary Human Sciences
StatePublished - 2 May 2022

Bibliographical note

Publisher Copyright:
Copyright © The Author(s), 2022. Published by Cambridge University Press.


  • Cultural evolution
  • hierarchical clustering
  • network


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