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 nodesʼ neighborhoods approaches the global type-distribution, provided that the network-based search is unbiased. However, younger nodesʼ 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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|Titolo:||Homophily and long-run integration in social networks|
|Citazione:||Yann, B., Sergio, C., Matthew O., J., Pin, P., & Brian W., R. (2012). Homophily and long-run integration in social networks. JOURNAL OF ECONOMIC THEORY, 147(5), 1754-1786.|
|Appare nelle tipologie:||1.1 Articolo in rivista|