Abstract
Human networks in general, and social networks in particular, have been demonstrated to be highly structured and easily decomposable into communities. As a consequence, it appeared natural to us to consider this inherent trait of social networks in order to better the hidden link prediction problem’s resolution. In this paper, we propose a supervised learning algorithm that incorporates community information as a feature alongside other similarity metrics. The objective is to show that the information about the network’s community structure will enhance the accuracy of the hidden link prediction in social networks. We tested the F1-score accuracy of the proposed model with four classifiers: SVM with an “rbf” kernel, Naive Bayes, random forest and K-NN. Experimental results demonstrate that the inclusion of community information can only improve the quality of the hidden link prediction.
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Kerkache, M.H., Sadeg-Belkacem, L., Benbouzid-Si Tayeb, F., Ali, A. (2022). Supervised Learning Using Community Detection for Link Prediction. In: Senouci, M.R., Boulahia, S.Y., Benatia, M.A. (eds) Advances in Computing Systems and Applications. CSA 2022. Lecture Notes in Networks and Systems, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-12097-8_8
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