Just a Few Seeds More: The Value of Network Data for Diffusion

• 成果类型: Article
• 署名作者: Akbarpour, Mohammad; Malladi, Suraj; Saberi, Amin
• 署名单位: Stanford University; Stanford University
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282; 1944-7981
• DOI: 10.1257/aer.20180798
• 发表日期: 2025-11
• 页码: 3713-3748
• 关键词: Social networks; contagion

摘要

Identifying the optimal set of individuals to first receive information (seeds) in a social network to maximize expected diffusion is a widely studied question in many settings. Several studies propose network-centrality-based heuristics to select seeds likely to increase diffusion. Here, we show that, for the classic independent cascade model of diffusion, either seeding a few more individuals at random can prompt a larger diffusion than optimal seeding or optimal seeding itself results in limited spread. These findings hold across a broad range of random networks and are supported by simulations on real-world networks. (JEL D83, D85, O12, O18, P25, P32, Z13)