Social distance and network structures
提出了一个可处理模型，分析个体对他人关系的感知如何决定网络结构，预测了违反三角不等式的社会距离下的小世界性质，并解释了度分布偏斜、度正同配性等实证模式。
This paper proposes a tractable model that allows us to analyze how agents' perception of relationships with others determines the structures of networks. In our model, agents are endowed with their own multidimensional characteristics and their payoffs depend on the social distance between them. We characterize the clustering coefficient and average path length in stable networks, and analyze how they are related to the way agents measure social distances. The model predicts the small-world properties under a class of social distance that violates the triangle inequality. Allowing for heterogeneity in link-formation costs, the model also accommodates other well documented empirical patterns of social networks such as skewed degree distributions, positive assortativity of degrees, and clustering-degree correlation.