The study of network formation is pervasive in economics, sociology, and many other fields. In this paper, we model network formation as a `choice' that is made by nodes in a network to connect to other nodes. We study these `choices' using discrete-choice models, in which an agent chooses between two or more discrete alternatives. We employ the `repeated-choice' (RC) model to study network formation. We argue that the RC model overcomes important limitations of the multinomial logit (MNL) model, which gives one framework for studying network formation, and that it is well-suited to study network formation. We also illustrate how to use the RC model to accurately study network formation using both synthetic and real-world networks. Using synthetic networks, we also compare the performance of the MNL model and the RC model. We find that the RC model estimates the data-generation process of our synthetic networks more accurately than the MNL model. We do a case study of a qualitatively interesting scenario -- the fact that new patents are more likely to cite older, more cited, and similar patents -- for which the RC model allows us to achieve interesting insights.
翻译:网络形成研究在经济学、社会学和许多其他领域十分普遍。 在本文中,我们将网络形成作为网络中结点与其他节点连接的“选择”模型。我们使用离散选择模型研究这些“选择”模型,其中代理商选择两种或两种以上离散替代品。我们使用“重复选择”模型(RC)模型研究网络形成。我们争辩说,RC模型克服了多名logit(MNL)模型的重要局限性,该模型为研究网络形成提供了一个框架,并且非常适合研究网络形成。我们还说明了如何使用RC模型来精确研究网络形成,同时使用合成网络和现实世界网络。我们还利用合成网络比较了MNL模型和RC模型的性能。我们发现,RC模型对我们合成网络的数据生成过程的估算比MNL模型更准确。我们对一个有质量意义的假设进行了案例研究,即新的专利更有可能引用更老、更引人入胜、更令人感兴趣的真知灼见的专利。