Abstract
From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the prediction of links as a knowledge base completion problem, and find that sharing neighbors is highly predictive of the formation of new links. Importantly, We distinguish between grounded neighbors and neighbors in the embedding space, and find that the latter is more predictive of tie formation. The paper discusses the implications of this finding in explaining the high levels of elite endogamy in Santiago.
Highlights
In countries where citizens inherit both parents’ last names, surnames are a source to uncover the population structure of society
We model the formation of affinity ties between surnames, and find that the formation of new links is highly predictable
These results indicate that surname affinity ties are strongly predictable
Summary
In countries where citizens inherit both parents’ last names, surnames are a source to uncover the population structure of society. We model the formation of affinity ties between surnames, and find that the formation of new links is highly predictable. The method predicts links as a knowledge base completion problem. Advances in network statistical modelling in the last decade have disentangled homophily from other, network-specific mechanisms that help segregate groups [3]. People tend to prefer connecting with their in-groups, they are prone to close triangles. This is the notion of triadic closure or “friend-of-a-friend” (FOAF) mechanism: A and B are likelier to become friends if they have C as a common friend [4,5,6,7]. FOAF independently exacerbates the separation of groups, because the friend-of-a-friend is often an in-group
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