Homophily and Long-Run Integration in Social Networks

Yann Bramoullé, Sergio Currarini, Matthew O. Jackson, Paolo Pin, Brian W. Rogers

We model network formation when heterogeneous nodes enter sequentially and form connections through both random meetings and network-based search, but with type-dependent biases. We show that there is "long-run integration," whereby the composition of types in sufficiently old nodes' neighborhoods approaches the global type distribution, provided that the network-based search is unbiased. However, younger nodes' connections still reflect the biased meetings process. We derive the type-based degree distributions and group-level homophily patterns when there are two types and location-based biases. Finally, we illustrate aspects of the model with an empirical application to data on citations in physics journals.

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