摘要 Many large scale phenomena, such as rapid changes

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論文資訊

• 類型：已發表論文
• 日期：2020

摘要

Many large scale phenomena, such as rapid changes in public opinion and the outbreak of 疾病 流行病學cs, can be fruitfully modeled as cascades of activation on 網絡s. This provides under- standing of how various connectivity patterns among 智能體s can influence the eventual extent of a cascade. We consider cascading dynamics on modular, degree-heterogeneous 網絡s, as such features are observed in many real-world 網絡s, and consider specifically the impact of the seeding strategy. We derive an analytic set of equations for the system by introducing a reduced description that extends a method developed by Gleeson that lets us accurately capture different seeding strategies using only one dynamical variable per module, namely the conditional exposure probability. We establish that activating the highest-deg

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