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 the underlying network structure as well as the available information on social peers to improve the information needs of a user. Models of centralized and distributed social networks are considered to implement this algorithm.


This algorithm can be utilized to grade items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, this work validates the results of analysis and expresses the efficiency of algorithms in approximating quantities of interest. The methods described are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.