Greedy approximation algorithms for finding dense components in a graph
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Detecting high log-densities: an O(n¼) approximation for densest k-subgraph
Proceedings of the forty-second ACM symposium on Theory of computing
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Proceedings of the 20th international conference on World wide web
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph cluster randomization: network exposure to multiple universes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
When web personalization misleads bucket testing
Proceedings of the 1st workshop on User engagement optimization
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Bucket testing, also known as split testing, A/B testing, or 0/1 testing, is a widely used method for evaluating users' satisfaction with new features, products, or services. In order not to expose the whole user base to the new service, the mean user satisfaction rate is estimated by exposing the service only to a few uniformly chosen random users. In a recent work, Backstrom and Kleinberg, defined the notion of network bucket testing for social services. In this context, users' interactions are only valid for measurement if some minimal number of their friends are also given the service. The goal is to estimate the mean user satisfaction rate while providing the service to the least number of users. This constraint makes uniform sampling, which is optimal for the traditional case, grossly inefficient. In this paper we introduce a simple general framework for designing and evaluating sampling techniques for network bucket testing. The framework is constructed in a way that sampling algorithms are only required to generate sets of users to which the service should be provided. Given an algorithm, the framework produces an unbiased user satisfaction rate estimator and a corresponding variance bound for any network and any user satisfaction function. Furthermore, we present several simple sampling algorithms that are evaluated using both synthetic and real social networks. Our experiments corroborate the theoretical results and demonstrate the effectiveness of the proposed framework and algorithms.