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Predicting affinity ties in a surname network.

Marcelo Mendoza1,2, Naim Bro1

  • 1Millennium Institute of Foundational Research on Data, Santiago, Chile.

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|September 2, 2021
PubMed
Summary

We built a surname network from Chilean records to study socioeconomic ties. Sharing neighbors in the network

Area of Science:

  • Social network analysis
  • Computational sociology
  • Socioeconomic stratification

Background:

  • Administrative registers of last names in Santiago, Chile, offer a unique dataset for analyzing social structures.
  • Understanding surname affinity networks can reveal patterns of social interaction and endogamy.
  • Socioeconomic factors significantly influence the formation of social ties and network structures.

Purpose of the Study:

  • To construct a surname affinity network encoding socioeconomic data from administrative registers.
  • To model link prediction within this network as a knowledge base completion problem.
  • To investigate the predictive power of different types of network neighbors on tie formation and explain elite endogamy.

Main Methods:

  • Creation of a multi-relational graph where nodes are surnames and edges represent interaction prevalence across socioeconomic deciles.

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  • Application of knowledge base completion techniques to predict the formation of new links (ties) between surnames.
  • Distinguishing between 'grounded' neighbors (direct connections) and 'embedding space' neighbors (latent representations) for predictive analysis.

    • Main Results:

    • Sharing of neighbors within the surname network is a strong predictor of new link formation.
    • Neighbors identified in the embedding space are more predictive of tie formation than 'grounded' neighbors.
    • The findings provide insights into the mechanisms driving elite endogamy in Santiago.

    Conclusions:

    • Network embedding techniques can effectively predict social tie formation, even beyond direct connections.
    • The predictive power of embedding space neighbors highlights the importance of latent social structures.
    • This approach offers a novel method for analyzing socioeconomic endogamy and social stratification using surname data.