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• W1493228775 abstract "Many complex networks in natural and social phenomena have often been characterized by heavy-tailed degree distributions. However, due to rapidly growing size of network data and concerns on privacy issues about using these data, it becomes more difficult to analyze complete data sets. Thus, it is crucial to devise effective and efficient estimation methods for heavy tails of degree distributions in large-scale networks only using local information of a small fraction of sampled nodes. Here we propose a tail-scope method based on local observational bias of the friendship paradox. We show that the tail-scope method outperforms the uniform node sampling for estimating heavy tails of degree distributions, while the opposite tendency is observed in the range of small degrees. In order to take advantages of both sampling methods, we devise the hybrid method that successfully recovers the whole range of degree distributions. Our tail-scope method shows how structural heterogeneities of large-scale complex networks can be used to effectively reveal the network structure only with limited local information." @default.
• W1493228775 created "2016-06-24" @default.
• W1493228775 creator A5017222566 @default.
• W1493228775 creator A5087786073 @default.
• W1493228775 date "2015-05-11" @default.
• W1493228775 modified "2023-10-17" @default.
• W1493228775 title "Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks" @default.
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• W1493228775 doi "https://doi.org/10.1038/srep09752" @default.
• W1493228775 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4426729" @default.
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• W1493228775 hasPublicationYear "2015" @default.
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