ABSTRACT
Recent work on unbiased sampling of OSNs has focused on estimation of the network characteristics such as degree distributions and clustering coefficients. In this work we shift the focus to node attributes. We show that existing sampling methods produce biased outputs and need modifications to alleviate the bias.
- V. D. Blondel et al. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.Google ScholarCross Ref
- S. Currarini et al. An economic model of friendship: Homophily, minorities, and segregation. Econometrica, 77(4):1003--1045, 2009.Google ScholarCross Ref
- M. Gjoka et al. Walking in facebook: A case study of unbiased sampling of osns. In IEEE INFOCOM 2010. Google ScholarDigital Library
- A. S. Maiya and T. Y. Berger-Wolf. Sampling community structure. In WWW 2010. Google ScholarDigital Library
Index Terms
- Sampling bias in user attribute estimation of OSNs
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