Estimating the Size of Online Social Networks

Ye, Shaozhi; Wu, Felix

doi:10.1109/socialcom.2010.32

Cited by 16 publications

(16 citation statements)

References 14 publications

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“…However, the running time of the the latter is unbounded in the worst case and expected to be more than the number of nodes in the graph. Recently, in [18] the authors try to estimate the size of social networks in a setup very similar to ours'. However, they either require that the users be sampled uniformly or use the algorithm from [14] which their experiments show is impractical.…”

Section: Background and Prior Workmentioning

confidence: 99%

Estimating sizes of social networks via biased sampling

Katzir¹,

Liberty²,

Somekh³

2011

Proceedings of the 20th International Conference on World Wide Web

117

106

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Online social networks have become very popular in recent years and their number of users is already measured in many hundreds of millions. For various commercial and sociological purposes, an independent estimate of their sizes is important. In this work, algorithms for estimating the number of users in such networks are considered. The proposed schemes are also applicable for estimating the sizes of networks' sub-populations. The suggested algorithms interact with the social networks via their public APIs only, and rely on no other external information. Due to obvious traffic and privacy concerns, the number of such interactions is severely limited. We therefore focus on minimizing the number of API interactions needed for producing good size estimates. We adopt the abstraction of social networks as undirected graphs and use random node sampling. By counting the number of collisions or non-unique nodes in the sample, we produce a size estimate. Then, we show analytically that the estimate error vanishes with high probability for smaller number of samples than those required by prior-art algorithms. Moreover, although our algorithms are provably correct for any graph, they excel when applied to social network-like graphs. The proposed algorithms were evaluated on synthetic as well real social networks such as Facebook, IMDB, and DBLP. Our experiments corroborated the theoretical results, and demonstrated the effectiveness of the algorithms.

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Section: Background and Prior Workmentioning

confidence: 99%

Estimating sizes of social networks via biased sampling

Katzir¹,

Liberty²,

Somekh³

2011

Proceedings of the 20th International Conference on World Wide Web

117

106

View full text Add to dashboard Cite

show abstract

“…This method is not applicable to YouTube, as video id's are randomly generated from a large id space. The authors of [25] propose a method to estimate the number of nodes in a given connected graph, and applies to a YouTube related video (sub)graph obtained using a sample YouTube video dataset from [17]. This method cannot be used to estimate the total number of YouTube videos.…”

Section: Related Workmentioning

confidence: 99%

Counting YouTube videos via random prefix sampling

Zhou

Adhikari

et al. 2011

Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference

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Leveraging the characteristics of YouTube video id space and exploiting a unique property of YouTube search API, in this paper we develop a random prefix sampling method to estimate the total number of videos hosted by YouTube. Through theoretical modeling and analysis, we demonstrate that the estimator based on this method is unbiased, and provide bounds on its variance and confidence interval. These bounds enable us to judiciously select sample sizes to control estimation errors. We evaluate our sampling method and validate the sampling results using two distinct collections of YouTube video id's (namely, treating each collection as if it were the "true" collection of YouTube videos). We then apply our sampling method to the live YouTube system, and estimate that there are a total of roughly 500 millions YouTube videos by May, 2011. Finally, using an unbiased collection of YouTube videos sampled by our method, we show that YouTube video view count statistics collected by prior methods (e.g., through crawling of related video links) are highly skewed, significantly under-estimating the number of videos with very small view counts (< 1000) ; we also shed lights on the bounds for the total storage YouTube must have and the network capacity needed to delivery YouTube videos.

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“…There are two different approaches in the literature for estimating the total number of registered users in the network (also known as graph size estimation): node collision based [Hardiman et al 2009;Katzir et al 2011;Massoulié et al 2006;Ye and Wu 2010] and edge collision based [Kurant et al 2012]. The estimators in Hardiman et al [2009], Katzir et al [2011], Massoulié et al [2006], and Ye and Wu [2010] use only the node IDs visited on the random walk and do not assume any prior information on the graph, while Kurant et al [2012] also assumes access to immediate friends list.…”

Section: Background and Prior Workmentioning

confidence: 99%

“…Permissions may be requested from Search engine public interfaces have been used in Gurevich [2008, 2011] to estimate corpus size, index freshness, and density of duplicates, and in Bar-Yossef and Gurevich [2009] estimate the impressionrank of a web page. Online social network public interfaces have been used in Gjoka et al [2010], Hardiman et al [2009], Ribeiro and Towsley [2010], and Wang et al [2014] to estimate the assortativity coefficient, degree distribution, and clustering coefficients of online social networks, as well as in Hardiman et al [2009], Katzir et al [2011], Kurant et al [2012], Massoulié et al [2006], and Ye and Wu [2010] to estimate the number of registered users.…”

Section: Introductionmentioning

confidence: 99%

Estimating Clustering Coefficients and Size of Social Networks via Random Walk

Katzir

Hardiman²

2015

ACM Trans. Web

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are the network average and global clustering coefficients and the number of registered users. The algorithms at hand (1) assume no prior knowledge about the network and (2) access the network using only the publicly available interface. More precisely, this work provides (a) a unified approach for clustering coefficients estimation and (b) a new network size estimator. The unified approach for the clustering coefficients yields the first external access algorithm for estimating the global clustering coefficient. The new network size estimator offers improved accuracy compared to prior art estimators.Our approach is to view a social network as an undirected graph and use the public interface to retrieve a random walk. To estimate the clustering coefficient, the connectivity of each node in the random walk sequence is tested in turn. We show that the error drops exponentially in the number of random walk steps. For the network size estimation we offer a generalized view of prior art estimators that in turn yields an improved estimator. All algorithms are validated on several publicly available social network datasets. ACM Reference Format:Liran Katzir and Stephen J. Hardiman. 2015. Estimating clustering coefficients and size of social networks via random walk.

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Estimating the Size of Online Social Networks

Cited by 16 publications

References 14 publications

Estimating sizes of social networks via biased sampling

Estimating sizes of social networks via biased sampling

Counting YouTube videos via random prefix sampling

Estimating Clustering Coefficients and Size of Social Networks via Random Walk

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