Distributions of centrality on networks

• 成果类型: Article
• 署名作者: Dasaratha, Krishna
• 署名单位: Harvard University
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2020.03.008
• 发表日期: 2020
• 页码: 1-27
• 关键词: Centrality; networks; social networks; peer effects; INEQUALITY; segregation

摘要

We provide a framework for determining agents' centralities in a broad family of random networks. Current understanding of network centrality is largely restricted to deterministic settings, but practitioners frequently use random network models. Our main theorems show that on large random networks, centrality measures are close to their expected values with high probability. We illustrate the economic consequences via three applications: (1) In network formation models with community structure, we show network segregation and differences in community size produce inequality. Benefits from peer effects accrue disproportionately to bigger and better-connected communities. (2) When link probabilities depend on spatial structure, we compute and compare the centralities of agents in different locations. (3) In models where connections depend on several independent characteristics, we can determine centralities 'characteristic-by-characteristic'. The basic techniques from these applications, which use the main theorems to reduce questions about random networks to deterministic calculations, extend to many network games. (C) 2020 Elsevier Inc. All rights reserved.