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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018-07-06
摘要
We present a physically inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed 網絡s. It assigns real-valued ranks to nodes rather than simply ordinal ranks, and it formalizes the assumption that interactions are more likely to occur between individuals with similar ranks. It provides a natural 統計 significance test for the inferred hierarchy, and it can be used to perform inference tasks such as predicting the existence or direction of edges. The ranking is obtained by solving a linear system of equations, which is sparse if the 網絡 is; thus, the resulting algorithm is extremely efficient and scalable. We illustrate these findings by analyzing real and synthetic data, including data sets from animal behavior, faculty hiring, 社會 support 網絡s, and sports to
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