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

Eom, Young-Ho and Jo, Hang-Hyun Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks. Scientific Reports, 5 (09752). pp. 1-9. ISSN 2045-2322 (2015)

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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.

Item Type: Article
Identification Number: 10.1038/srep09752
Uncontrolled Keywords: Computational science; Scientific data; Information theory and computation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Economics and Institutional Change
Depositing User: Caterina Tangheroni
Date Deposited: 08 Jun 2015 13:19
Last Modified: 08 Jun 2015 13:19
URI: http://eprints.imtlucca.it/id/eprint/2702

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