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Link prediction problem has increasingly become prominent in many domainssuch as social network analyses, bioinformatics experiments, transportationnetworks, criminal investigations and so forth. A variety of techniques hasbeen developed for link prediction problem, categorized into 1) similaritybased approaches which study a set of features to extract similar nodes; 2)learning based approaches which extract patterns from the input data; 3)probabilistic statistical approaches which optimize a set of parameters toestablish a model which can best compute formation probability. However,existing literatures lack approaches which utilize strength of each approach byintegrating them to achieve a much more productive one. To tackle the linkprediction problem, we propose an approach based on the combination of firstand second group methods; the existing studied works use just one of thesecategories. Our two-phase developed method firstly determines new featuresrelated to the position and dynamic behavior of nodes, which enforce theapproach more efficiency compared to approaches using mere measures. Then, asubspace clustering algorithm is applied to group social objects based on thecomputed similarity measures which differentiate the strength of clusters;basically, the usage of local and global indices and the clustering informationplays an imperative role in our link prediction process. Some extensiveexperiments held on real datasets including Facebook, Brightkite and HepThindicate good performances of our proposal method. Besides, we haveexperimentally verified our approach with some previous techniques in the areato prove the supremacy of ours.

Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach