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Faculty of Science, University of Brunei Darussalam, Brunei Darussalam   
" It is a great honor to serve as the editor-in-chief of IJIEE. I'll work together with the editorial team. Hopefully, IJIEE will be recognized among the readers in the related field."
IJIEE 2016 Vol.6(4): 265-268 ISSN: 2010-3719
DOI: 10.18178/IJIEE.2016.6.4.635

Review Social Network Analysis and Mining: Link Prediction

Kalum P. Udagepola and Fatima Chiroma
Abstract—The rapid generation and uncontrollable accumulation of the social network data has raised a real issue now, because the data are vast, noisy, distributed, unstructured and dynamic. Since this data can be mined by using web mining techniques, social network analysis and link prediction algorithms, in this article we try to understand the social structure and issues surrounding mining social network data. We will also be looking at the link prediction problems in dynamic social networks and the important techniques that can be applied as an attempt for a resolution.

Index Terms—Social network, social network analysis, Link prediction, web mining.

K. P. Udagepola is with the American University of Nigeria and School of Information Technology and Computing, Yola, Nigeria (e-mail: kalum.udagepola@ aun.edu.ng).
F. Chiroma is with the American University of Nigeria and Department of Information Systems, Yola, Nigeria (e-mail: Fatima.Chiroma@aun.edu.ng).


Cite:Kalum P. Udagepola and Fatima Chiroma, "Review Social Network Analysis and Mining: Link Prediction," International Journal of Information and Electronics Engineering vol. 6, no. 4, pp. 265-268, 2016.

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