本頁只刊出中文翻譯與中文說明；英文原文請見下方原文連結。
原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-01-22
摘要
One can often make inferences about a growing 網絡 from its current state alone. For example, it is generally possible to determine how a 網絡 changed over time or pick among plausible mechanisms explaining its growth. In practice, however, the extent to which such problems can be solved is limited by existing techniques, which are often inexact, inefficient, or both. In this Letter, we derive exact and efficient inference methods for growing trees and demonstrate them in a series of applications: 網絡 interpolation, history reconstruction, model fitting, and model selection.
※ 此為已發表論文，全文需透過期刊付費取得