A note on extending Knuth's tree estimator to directed acyclic graphs
Information Processing Letters
A technique for measuring the relative size and overlap of public Web search engines
WWW7 Proceedings of the seventh international conference on World Wide Web 7
On the Estimate of a Directed Graph
WG '88 Proceedings of the 14th International Workshop on Graph-Theoretic Concepts in Computer Science
Estimating the efficiency of backtrack programs.
Estimating the efficiency of backtrack programs.
Measuring and extracting proximity in networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient search engine measurements
Proceedings of the 16th international conference on World Wide Web
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Random sampling from a search engine's index
Journal of the ACM (JACM)
Scalable proximity estimation and link prediction in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Estimating the Size of Online Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Walking on a graph with a magnifying glass: stratified sampling via weighted random walks
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Counting YouTube videos via random prefix sampling
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
DEMON: a local-first discovery method for overlapping communities
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Coarse-grained topology estimation via graph sampling
Proceedings of the 2012 ACM workshop on Workshop on online social networks
Sampling online social networks by random walk
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
Using Location-Based Social Networks to Validate Human Mobility and Relationships Models
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Highly dynamic distributed computing with byzantine failures
Proceedings of the 2013 ACM symposium on Principles of distributed computing
Estimating clustering coefficients and size of social networks via random walk
Proceedings of the 22nd international conference on World Wide Web
Mining a search engine's corpus without a query pool
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Towards social data platform: automatic topic-focused monitor for twitter stream
Proceedings of the VLDB Endowment
On estimating the average degree
Proceedings of the 23rd international conference on World wide web
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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.