摘要 隨機圖 null models have found widespread application

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論文資訊

• 類型：已發表論文
• 日期：2018

摘要

隨機圖 null models have found widespread application in diverse research communities analyzing 網絡 datasets, including 社會, 資訊, and 經濟 網絡s, as well as 食物網s, 蛋白質-蛋白質 interactions, and neuronal 網絡s. The most popular 隨機圖 null models, called configuration models, are defined as uniform distributions over a space of graphs with a fixed degree sequence. Commonly, properties of an empirical 網絡 are compared to properties of an ensemble of graphs from a configuration model in order to quantify whether empirical 網絡 properties are meaningful or whether they are instead a common consequence of the particular degree sequence. In this work we study the subtle but important decisions underlying the specification of a configuration model, and we investigate the role these choices play in graph sampling procedu

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