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原文連結
論文資訊
- 類型：已發表論文
- 日期：2013-07-18
摘要
生物 and 社會 網絡s are composed of heterogeneous nodes that contribute differentially to 網絡 structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes-from ranking websites to determining critical 物種 in ecosystems-yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the 網絡 about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or
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