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Performance Analysis and Comparison of Sampling Algorithms in Online Social Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Stuti K., Atul Srivastava

Stuti K. and Atul Srivastava. Article: Performance Analysis and Comparison of Sampling Algorithms in Online Social Network. International Journal of Computer Applications 133(5):30-35, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Stuti K. and Atul Srivastava},
	title = {Article: 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},
	month = {January},
	note = {Published by 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.


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Comparison of Sampling Algorithms, Node Degree Distribution, Clustering Coefficient