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Homophily and long-run integration in social networks

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Author Info

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

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 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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File URL: http://www.sciencedirect.com/science/article/pii/S0022053112000610
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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Theory.

Volume (Year): 147 (2012)
Issue (Month): 5 ()
Pages: 1754-1786

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Handle: RePEc:eee:jetheo:v:147:y:2012:i:5:p:1754-1786

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Web page: http://www.elsevier.com/locate/inca/622869

Related research

Keywords: Network formation; Social networks; Homophily; Integration; Degree distribution; Citations;

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Cited by:
  1. Lafond, Francois, 2012. "Learning and the structure of citation networks," UNU-MERIT Working Paper Series 071, United Nations University, Maastricht Economic and social Research and training centre on Innovation and Technology.

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