Homophily and Long-Run Integration in Social Networks

TL;DR

This paper models how social networks evolve with heterogeneous nodes, showing that unbiased network-based search leads to long-term integration of types, while initial biases influence younger nodes' connections, with empirical validation in citation data.

Contribution

It introduces a model combining random meetings and biased network search, analyzing long-term type integration and deriving degree distributions and homophily patterns.

Findings

Long-run integration occurs with unbiased search.

Younger nodes' connections reflect initial meeting biases.

Empirical application to physics journal citations supports the model.

Abstract

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.