JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis

JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis

Link Prediction

Network Analysis

Ensemble

Machine Learning

Graph Analy-sis

Voting Techniques

In this research, a new technique to improve the accuracy of the link prediction for most of the networks is proposed; it is based on the prediction ensemble approach using the voting merging technique. The new proposed ensemble called Jaccard, Katz, and Random models Wrapper (JKRW), it scales up the prediction accuracy and provides better predictions for different sizes of populations including small, medium, and large data. The proposed model has been tested and evaluated based on the area under curve (AUC) and accuracy (ACC) measures. These measures applied to the other models used in this study that has been built based on the Jaccard Coefficient, Katz, Adamic/Adar, and Preferential attachment. Results from applying the evaluation matrices verify the improvement of JKRW effectiveness and stability in comparison to the other tested models.  The results from applying the Wilcoxon signed-rank method (one of the non-parametric paired tests) indicate that JKRW has significant differences compared to the other models in the different populations at 0.95 confident interval.

Taleb, Aya

Al-Sayyed, Rizik M. H.

Al-Bdour, Hamed S.

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 12 (2021); pp. 125-139

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2021-06-18

Copyright (c) 2021 Aya Taleb, Rizik M. H. Al-Sayyed, Hamed S. Al-Bdour

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article