  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.
