Skip to Main content Skip to Navigation
Conference papers

Subsampling for Chain-Referral Methods

Abstract : We study chain-referral methods for sampling in social networks. These methods rely on subjects of the study recruiting other participants among their set of connections. This approach gives us the possibility to perform sampling when the other methods, that imply the knowledge of the whole network or its global characteristics, fail. Chain-referral methods can be implemented with random walks or crawling in the case of online social networks. However, the estimations made on the collected samples can have high variance, especially with small sample size. The other drawback is the potential bias due to the way the samples are collected. We suggest and analyze a sub-sampling technique, where some users are requested only to recruit other users but do not participate to the study. Assuming that the referral has lower cost than actual participation, this technique takes advantage of exploring a larger variety of population, thus decreasing significantly the variance of the estimator. We test the method on real social networks and on synthetic ones. As by-product, we propose a Gibbs like method for generating synthetic networks with desired properties.
Document type :
Conference papers
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download
Contributor : Alina Tuholukova Connect in order to contact the contributor
Submitted on : Wednesday, November 23, 2016 - 10:22:16 AM
Last modification on : Thursday, January 20, 2022 - 4:12:22 PM
Long-term archiving on: : Monday, March 20, 2017 - 5:02:44 PM


Files produced by the author(s)




Konstantin Avrachenkov, Giovanni Neglia, Alina Tuholukova. Subsampling for Chain-Referral Methods. International Conference on Analytical and Stochastic Modeling Techniques and Applications, Aug 2016, Cardiff, United Kingdom. pp.17 - 31, ⟨10.1007/978-3-319-43904-4_2⟩. ⟨hal-01401287⟩



Record views


Files downloads