Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
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
Approximating average parameters of graphs
Random Structures & Algorithms
Random sampling from a search engine's index
Journal of the ACM (JACM)
Concentration of Measure for the Analysis of Randomized Algorithms
Concentration of Measure for the Analysis of Randomized Algorithms
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
Estimating sizes of social networks via biased sampling
Proceedings of the 20th international conference on World wide web
Efficient Search Engine Measurements
ACM Transactions on the Web (TWEB)
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating sum by weighted sampling
ICALP'07 Proceedings of the 34th international conference on Automata, Languages and Programming
Estimating clustering coefficients and size of social networks via random walk
Proceedings of the 22nd international conference on World Wide Web
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Networks are characterized by nodes and edges. While there has been a spate of recent work on estimating the number of nodes in a network, the edge-estimation question appears to be largely unaddressed. In this work we consider the problem of estimating the average degree of a large network using efficient random sampling, where the number of nodes is not known to the algorithm. We propose a new estimator for this problem that relies on access to node samples under a prescribed distribution. Next, we show how to efficiently realize this ideal estimator in a random walk setting. Our estimator has a natural and simple implementation using random walks; we bound its performance in terms of the mixing time of the underlying graph. We then show that our estimators are both provably and practically better than many natural estimators for the problem. Our work contrasts with existing theoretical work on estimating average degree, which assume that a uniform random sample of nodes is available and the number of nodes is known.