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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-11-13
摘要
Most empirical studies of complex 網絡s do not return direct, error-free measurements of 網絡 structure. Instead, they typically rely on indirect measurements that are often error prone and unreliable. A fundamental problem in empirical 網絡 science is how to make the best possible estimates of 網絡 structure given such unreliable data. In this article, we describe a fully 貝氏 method for reconstructing 網絡s from observational data in any format, even when the data contain substantial measurement error and when the nature and magnitude of that error is unknown. The method is introduced through pedagogical case studies using real-world example 網絡s, and specifically tailored to allow straightforward, 計算ly efficient implementation with a minimum of technical input. Computer code implementing the method
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