A Classifier Ensembling Approach for Imbalanced Social Link Prediction

Supervised learning is a commonly used tool for link prediction in social networks, where data imbalance is a major challenge because only a small portion of nodes may have social connections. In this paper, we propose to use a k-nearest neighbor sampling and a random sampling combined approach to address data imbalance issue for social link prediction. In our solution, we use two sampling approaches to generate multiple copies of the network data, and then use a number of similarity link prediction measures to generate independent variable (features) as training data. For each copy of the sampled data, we train a classifier and use a classifier ensemble for final link prediction. Experimental results on a co-authorship network validate the performance of the proposed design.