論文

査読有り
2016年

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

PROCEEDINGS OF ECCS 2014: EUROPEAN CONFERENCE ON COMPLEX SYSTEMS
  • Thong Pham
  • ,
  • Paul Sheridan
  • ,
  • Hidetoshi Shimodaira

開始ページ
141
終了ページ
153
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1007/978-3-319-29228-1_13
出版者・発行元
SPRINGER

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.


リンク情報
DOI
https://doi.org/10.1007/978-3-319-29228-1_13
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000385253000013&DestApp=WOS_CPL
URL
http://orcid.org/0000-0002-1940-9290
ID情報
  • DOI : 10.1007/978-3-319-29228-1_13
  • ISSN : 2213-8684
  • ORCIDのPut Code : 44479124
  • Web of Science ID : WOS:000385253000013
  • ORCIDで取得されたその他外部ID : a:1:{i:0;a:1:{s:4:"isbn";s:13:"9783319292267";}}

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