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：2022-01-19
摘要
Understanding noise in 網絡s and finding the right scale to represent a system are important problems in 網絡 biology. Most research focuses on the raw, micro-scale 網絡 from data/simulations and seldom explores the scale dependence of properties. Here, we introduce the einet package, which looks at the most informative scale in a 生物 網絡 using recent concepts from 資訊 theory and 網絡 science. einet uses two metrics: Effective 資訊, which measures the interplay between degeneracy and determinism in a 網絡's edges, and causal 湧現, which finds the scale of the 網絡 with the highest effective 資訊. einet is available in R and Python and provides tools to explore noise and scale dependency in 網絡s as well as compare 資訊 flow and noise across 網絡s.
※ 此為已發表論文，全文需透過期刊付費取得