Low selectivity problem using the concept of sampling
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 3
Abstract
available information on social peers for improving the information needs of a user or node, this is because of the birth of online social networks. In this paper, the focus is on improvement of the performance of collecting information from the neighborhood of a user or a node in a dynamic social network. To explore user’s or node’s social network correctly we have introduced sampling based algorithms by keeping in mind the structure of social network and to approximate the quantities of interest in short time. By showing correlations across our samples we have introduced and also analyzed variants of basic sampling scheme. Here, the models of distributed and centralized network are considered. Assuming that information for each user or node is available we have showed that our algorithms can be utilized to rank nodes which are neighbor to the user. We demonstrate the working of our algorithms for approximation of various quantities of interest and validate analysis results. This is done using real and synthetic data. The methods we describe can be possibly easily adopted in verities of strategies which aim to collect information efficiently from a social graph.
Authors and Affiliations
Mr. Y. P. Murumakar, , Prof. Y. B. Gurav,
Keywords
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- DOI 10.9790/0661-16317882
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