Estimating sizes of social networks via biased sampling
Proceedings of the 20th international conference on World wide web
Counting YouTube videos via random prefix sampling
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Mining a search engine's corpus without a query pool
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
On estimating the average degree
Proceedings of the 23rd international conference on World wide web
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The huge size of online social networks (OSNs) makes it prohibitively expensive to precisely measure any properties which require the knowledge of the entire graph. To estimate the size of an OSN, i.e., the number of users an OSN has, this paper introduces two estimators using widely available OSN functionalities/services. The first estimator is a maximum likelihood estimator (MLE) based on uniform sampling. An O(logn) algorithm is developed to solve the estimator, which is 70 times faster than the naive linear probing algorithm in our experiments. The second estimator is based on random walkers and we generalize it to estimate other graph properties. In-depth evaluations are conducted on six real OSNs to show the bias and variance of these two estimators. Our analysis addresses the challenges and pitfalls when developing and implementing such estimators for OSNs.