Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • Pohang University of Science and Technology
  • Université de Toulouse
  • Institute for advanced studies Lucca


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.


Original languageEnglish
Article number09752
Pages (from-to)1-9
JournalScientific Reports
Publication statusPublished - 11 May 2015
MoE publication typeA1 Journal article-refereed

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