title: Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks.
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.
authors.affiliation: 1] Laboratoire de Physique Théorique du CNRS, IRSAMC, Université de Toulouse, UPS, F-31062 Toulouse, France [2] IMT Institute for Advanced Studies Lucca, Piazza San Francesco 19, Lucca 55100, Italy.
authors.forename: Young-Ho
authors.initials: YH
authors.lastname: Eom
authors.affiliation: 1] BK21plus Physics Division and Department of Physics, Pohang University of Science and Technology, Pohang 790-784, Republic of Korea [2] BECS, Aalto University School of Science, P.O. Box 12200, FI-00076, Finland.
authors.forename: Hang-Hyun
authors.initials: HH
authors.lastname: Jo
mesh_terms: D033062:Friends; D006801:Humans; D012944:Social Support; D013223:Statistics as Topic