A meta-heuristic framework based on clustering and preprocessed datasets for solving the link prediction problem

Abstract

This study presents a solution to a problem commonly known as link prediction problem. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Finding a solution to link prediction problem attracts variety of computer science fields such as data mining and machine learning. This attraction is due to its importance for many applications such as social networks, bioinformatics and co-authorship networks. Towards solving this problem, Evolutionary Link Prediction (EVO-LP) framework is proposed, presented, analysed and tested. EVO-LP is a framework that includes dataset preprocessing approach and a meta-heuristic algorithm based on clustering for prediction. EVO-LP is divided into preprocessing stage and link prediction stage. Feature extraction, data under-sampling and feature selection are utilised in the preprocessing stage, while in the prediction stage, a meta-heuristic algorithm based on clustering is used as an optimiser. Experimental results on a number of real networks show that EVO-LP improves the prediction quality with low time complexity.