Efficient link prediction method in dark networks analysis.

A. W. Mahesar; Z. Bhatti; A. Waqas; M. Y. Koondhar; M. M. Rind; S. Nizamani

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Efficient link prediction method in dark networks analysis.

Author(s)

A. W. Mahesar Department of Computer Science, Institute of Information and Communication Technology, International Islamic University Malaysia

Z. Bhatti Institute of Information and Communication Technology, University of Sindh, Jamshoro, Pakistan

A. Waqas Institute of Information and Communication Technology, International Islamic University, Kuala Lumpur, Malaysia

M. Y. Koondhar Institute of Information and Communication Technology, International Islamic University, Kuala Lumpur, Malaysia

M. M. Rind Institute of Information and Communication Technology, International Islamic University, Kuala Lumpur, Malaysia

S. Nizamani Department Of Computer Science, Mirpurkhas Campus, University Of Sindh, Pakistan

Abstract

- The prediction of future links is very important problem in the field of complex network analysis. Due to the intricate interplay between nodes in dark networks the links prediction problem become very hard. As the dark networks evolve slowly with few nodes and gradually formed as well organized networks due to scale-free topology and therefore need efficient method of links prediction. In this paper, we apply two useful metrics of links prediction namely Jaccard’s coefficient and Adamic/Aadr on the 9/11 dark network dataset. The results of both these metrics has been compared and it has been observed that Adamic/Aadr metric is more efficient method of link prediction in case of dark networks as compared to Jaccard’s coefficient. The performance of both these methods is evaluated by using standard evaluation metrics which is precision.

Publication Details

Page(s) 81-84

DOI DOI not available

Published Journal: Sindh University Research Journal, Volume: 48, Issue: 1, Year: 2016

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