Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity

被引：0

作者：

Thong Pham ^{[1
]}

Sheridan, Paul ^{[2
]}

Shimodaira, Hidetoshi ^{[1
]}

机构：

[1] Osaka Univ, Osaka, Japan

[2] Univ Tokyo, Tokyo, Japan

来源：

PROCEEDINGS OF ECCS 2014: EUROPEAN CONFERENCE ON COMPLEX SYSTEMS | 2016年

关键词：

DISTRIBUTIONS;

D O I：

10.1007/978-3-319-29228-1_13

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We introduce a statistically sound method called PAFit for the joint estimation of preferential attachment and node fitness in temporal complex networks. Together these mechanisms play a crucial role in shaping network topology by governing the way in which nodes acquire new edges over time. PAFit is an advance over previous methods in so far as it does not make any assumptions on the functional form of the preferential attachment function. We found that the application of PAFit to a publicly available Flickr social network dataset turned up clear evidence for a deviation of the preferential attachment function from the popularly assumed log-linear form. What is more, we were surprised to find that hubs are not always the nodes with the highest node fitnesses. PAFit is implemented in an R package of the same name.

引用

页码：141 / 153

页数：13

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