论文标题
混合logit模型和网络形成
Mixed Logit Models and Network Formation
论文作者
论文摘要
网络形成的研究在经济学，社会学和许多其他领域都普遍存在。在本文中，我们将网络形成建模为网络中节点以连接其他节点的“选择”。我们使用离散选择模型研究这些“选择”，其中代理在两个或多个离散的替代方案之间选择。我们采用“重复选择”（RC）模型来研究网络形成。我们认为RC模型克服了多项式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模型使用合成和现实世界网络准确研究网络形成。使用与边缘无关的合成网络，我们还比较了MNL模型和RC模型的性能。我们发现RC模型比MNL模型更准确地估算合成网络的数据生成过程。在依次形成的专利引用网络中，我们介绍了一个案例研究，案例研究是一种定性有趣的场景 - 新专利更有可能引用较旧，更被引用和类似专利的事实 - 使用RC模型会产生有趣的见解。
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 edge-independent 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. In a patent citation network, which forms sequentially, we present 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 employing the RC model yields interesting insights.
