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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dong Wang Zhenyu Li Gaogang Xie |
| Copyright Year | 2011 |
| Abstract | 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. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 399432 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781612842325 |
| ISSN | 15503607 |
| e-ISBN | 9781612842332 |
| e-ISBN | 9781612842318 |
| DOI | 10.1109/icc.2011.5963203 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-05 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Peer to peer computing Markov processes Sampling methods Sufficient conditions Social network services Algorithm design and analysis IEEE Communications Society |
| Content Type | Text |
| Resource Type | Article |
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