International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 133 - Issue 5 |
Published: January 2016 |
Authors: Stuti K., Atul Srivastava |
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Stuti K., Atul Srivastava . Performance Analysis and Comparison of Sampling Algorithms in Online Social Network. International Journal of Computer Applications. 133, 5 (January 2016), 30-35. DOI=10.5120/ijca2016907868
@article{ 10.5120/ijca2016907868, author = { Stuti K.,Atul Srivastava }, title = { Performance Analysis and Comparison of Sampling Algorithms in Online Social Network }, journal = { International Journal of Computer Applications }, year = { 2016 }, volume = { 133 }, number = { 5 }, pages = { 30-35 }, doi = { 10.5120/ijca2016907868 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2016 %A Stuti K. %A Atul Srivastava %T Performance Analysis and Comparison of Sampling Algorithms in Online Social Network%T %J International Journal of Computer Applications %V 133 %N 5 %P 30-35 %R 10.5120/ijca2016907868 %I Foundation of Computer Science (FCS), NY, USA
Graph sampling provides an efficient way by selecting a representative subset of the original graph thus making the graph scale small for improved computations. Random walk graph sampling has been considered as a fundamental tool to collect uniform node samples from a large graph. In this paper, a comprehensive analysis and comparison of four existing sampling algorithms- BFS, NBRW-rw, MHRW and MHDA is presented. The comparison is shown on the basis of the performance of each algorithm on different kinds of datasets. Here, the considered parameters are node-degree distribution and clustering coefficient which effect the performance of an algorithm in generating unbiased samples. The sampling methods as in this study are analysed on the real-network datasets and finally the conclusion says that MHDA performs excellently whereas BFS gives a poor performance.