Abstract In complex networks, link prediction is used to predict future relationships or missing edges. Due to applications in multiple domains, link prediction has attracted the attention of researchers from the domain. Plenty of algorithms have been proposed for link prediction in the past, but predicting missing links in networks correctly and efficiently is still a challenging problem. The proposed algorithm is suitable for both unipartite and bipartite networks and would perform well on transportation, financial and biological networks. Also, the algorithm has been extended for weighted networks. In this work, we present a novel Similarity-based method for link prediction based on the Current-flow Centrality, Shortest distance, and Clustering coefficient. We performed extensive experiments and the performance of the algorithm has been evaluated using four metrics, namely AUC, Precision, Prediction power and Prediction@K. The Proposed algorithm has been tested on 16 datasets and compared with 14 baseline algorithms. The experimental results show that the novel algorithm outperforms the baseline algorithms based on the given 4 metrics.
Kumar et al. (Tue,) studied this question.