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This paper concentrates on improving the performance of information collection from the neighborhood of a user in a dynamic social network. By introducing sampling based algorithms to efficiently explore a user's social network respecting its structure and to quickly approximate quantities of interest. It introduces and analyzes variants of the basic sampling scheme exploring correlations across the samples. As online social networking emerges, there has been increased interest to utilize thedoi:10.18535/ijecs/v5i9.40 fatcat:emzd4m2owralxgxziejkokwj6m