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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014-02-18
摘要
We study a simple model of how 社會 behaviors, like trends and opinions, propagate in 網絡s where individuals adopt the trend when they are informed by threshold T neighbors who are adopters. Using a dynamic message-passing algorithm, we develop a tractable and 計算ly efficient method that provides complete time 演化 of each individual's probability of adopting the trend or of the frequency of adopters and nonadopters in any arbitrary 網絡s. We validate the method by comparing it with 蒙地卡羅-based 智能體 simulation in real and synthetic 網絡s and provide an exact analytic scheme for large random 網絡s, where simulation results match well. Our approach is general enough to incorporate non-馬可夫ian processes and to include heterogeneous thresholds and thus can be applied to explore rich sets of complex heterogen
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