Towards unbiased sampling of online social networks

  • Wang D
  • Li Z
  • Xie G
  • 15


    Mendeley users who have this article in their library.
  • 4


    Citations of this article.


Unbiased sampling of online social networks (OSNs) makes it possible to get accurate statistical properties of large-scale OSNs. However, the most used sampling methods, Breadth-First-Search (BFS) and Greedy, are known to be biased towards high degree nodes, yielding inaccurate statistical results. To give a general requirement for unbiased sampling, we model the crawling process as a Markov Chain and deduce a necessary and sufficient condition, which enables us to design various efficient unbiased sampling methods. To the best of our knowledge, we are among the first to give such a condition. Metropolis-Hastings Random Walk (MHRW) is an example which satisfies the condition. However, walkers in MHRW may stay at some low-degree nodes for a long time, resulting considerable self-loops on these nodes, which adversely affect the crawling efficiency. Based on the condition, a new unbiased sampling method, called USRS, is proposed to reduce the probabilities of self-loops. We use the dataset of Renren, the largest OSN in China, to evaluate the performance of USRS. The results have demonstrated that USRS generates unbiased samples with low self-loop probabilities, and achieves higher crawling efficiency.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Dong Wang

  • Zhenyu Li

  • Gaogang Xie

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free