Abstract
Providing a solution for the link prediction problem attracts several computer science fields and becomes a popular challenge in researches. This challenge is presented by introducing several approaches keen to provide the most precise prediction quality within a short period of time. The difficulty of the link prediction problem comes from the sparse nature of most complex networks such as social networks. This paper presents a parallel metaheuristic framework which is based on moth-flame optimization (MFO), clustering and pre-processed datasets to solve the link prediction problem. This framework is implemented and tested on a high-performance computing cluster and carried out on large and complex networks from different fields such as social, citation, biological, and information and publication networks. This framework is called Parallel MFO for Link Prediction (PMFO-LP). PMFO-LP is composed of data preprocessing stage and prediction stage. Dataset division with stratified sampling, feature extraction, data under-sampling, and feature selection are performed in the data preprocessing stage. In the prediction stage, the MFO based on clustering is used as the prediction optimizer. The PMFO-LP provides a solution to the link prediction problem with more accurate prediction results within a reasonable amount of time. Experimental results show that PMFO-LP algorithm outperforms other well-regarded algorithms in terms of error rate, the area under curve and speedup. Note that the source code of the PMFO-LP algorithm is available at https://github.com/RehamBarham/PMFO_MPI.cpp.