Predicting affinity ties in a surname network
This work addresses the issue of understanding elite endogamy in Santiago, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of predicting affinity ties in a surname network from administrative data in Santiago, Chile, by modeling it as a knowledge base completion problem, finding that neighbors in the embedding space are more predictive of new link formation than grounded neighbors.
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.