How Does Network Structure Impact Socially Reinforced Diffusion?

• 成果类型: Article
• 署名作者: Sassine, Jad Georges; Rahmandad, Hazhir
• 署名单位: Amazon.com; Massachusetts Institute of Technology (MIT)
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2023.1658
• 发表日期: 2024
• 关键词: diffusion; threshold models; Repeated interactions

摘要

How does network structure impact the speed and reach of social contagions? The current view holds that random links facilitate simple contagion, but when agents require multiple reinforcements for complex adoption, clustered networks are better conduits of social influence. We show that in complex contagion, even low probabilities of adoption upon a single contact would activate an exponential contagion process that tilts the balance in favor of random networks. On the other hand, underappreciated but critical to the race between random and clustered networks is how long agents engage with contagion. Switching back to prior practice and the inactivation of senders and especially receivers shorten the window of engagement for convincing distant contacts and weaken the reach of diffusion on random networks. We propose a simplified framework where clustering primarily enables contagion when repetition matters and receivers lose interest quickly; otherwise, diffusion, simple or complex, is faster on random networks than clustered ones. These mechanisms can inform designing social networks, structuring groups, and seeding of ideas and innovations at a time when the increasing inflow of content from various media limits actors' engagement with each item, whereas expanding network size and connections speeds up diffusion through distant contacts.