A pairwise strategic network formation model with group heterogeneity: With an application to international travel

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY

Network Science Pub Date : 2020-12-29 DOI:10.1017/nws.2022.16

Tadao Hoshino

引用次数: 1

Abstract

Abstract This study considers a network formation model in which each dyad of agents strategically determines the link status. Our model allows the agents to have unobserved group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood method, in which the latent group structure is estimated using the binary segmentation algorithm in the second step. As an empirical illustration, we focus on the network data of international visa-free travels. The results indicate the presence of significant strategic complementarity and a certain level of degree heterogeneity in the network formation behavior.

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具有群体异质性的成对战略网络形成模型及其在国际旅行中的应用

摘要本研究考虑了一个网络形成模型，在该模型中，每个二元代理从战略上决定了链路状态。我们的模型允许代理在链接形成的倾向中具有未观察到的群体异质性。对于模型估计，我们提出了一种三步最大似然方法，其中在第二步中使用二进制分割算法来估计潜在的群结构。作为实证说明，我们关注的是国际免签证旅行的网络数据。研究结果表明，网络形成行为存在显著的战略互补性和一定程度的异质性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

3.50

自引率

5.90%

发文量

期刊介绍： Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.