A Novel PSO based Intelligence Link Prediction Algorithm in Real World Networks

Abstract

Link prediction is a critical challenge to analyse social networks with applications in areas such as information retrieval, bioinformatics, e-commerce etc. To overcome the challenges of either predicting the missing links or finding the future connections, a variety of approaches have been presented so far. Similarity-based approaches, which evaluate the network structure and use the number of shared neighbours between two nodes as a key criterion to determine structural similarity , are the most efficient among these methods. In this paper, we have proposed a novel link prediction algorithm based on Particle Swarm Optimization (PSO) and implemented it on different real world datasets. Our proposed approach achieved 72.75% accuracy in Zachary karate Club, 75.26% accuracy in Bottlenose Dolphin Network, 80.35% accuracy in College Football Network, and 78.36% accuracy in Kreb’s book Network which beat the performances of traditional algorithms.