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

摘要: 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 concerns on privacy issues about using these data, it becomes more difficult analyze complete sets. Thus, is crucial devise effective efficient estimation methods for heavy tails distributions large-scale only local information a small fraction sampled nodes. Here we propose tail-scope method based observational bias the friendship paradox. We show that outperforms uniform node sampling estimating distributions, while opposite tendency observed range degrees. In order take advantages both methods, hybrid successfully recovers whole Our shows how structural heterogeneities can be used effectively reveal structure with limited information.