Predicting scientific research trends based on link prediction in keyword networks

Abstract

• Article keywords as knowledge elements could be beneficial for bibliometric analysis. • Keyword networks are small-worlds but not scale-free networks. • We proposed a new link prediction algorithm based of nodes topological features. • Machine learning based methods could be useful for future studies to find new domains. • Journal and conference outlets are both valuable sources for bibliometric analysis. The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem. First, we have utilized three topology-based link prediction algorithms, two of which are commonly used in literature. We have also proposed a third algorithm based on nodes (keywords) clustering coefficient, their centrality measures like eigenvector centrality, and nodes community information. Then, we used nodes topological features and the outputs of aforementioned topology-based link prediction algorithms as features to feed five machine learning link prediction algorithms (SVM, Random Forest Classifier, K-Nearest Neighbors, Gaussian Naïve Bayes, and Multinomial Naïve Bayes). All tested predictors have shown considerable performance and their results are discussed in this paper.