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原文連結
論文資訊
- 類型：已發表論文
- 日期：2012
摘要
We model 網絡 formation when heterogeneous nodes enter sequentially and form connections through both random meetings and 網絡-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 網絡-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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