Predicting Citation Count of Scientists as a Link Prediction Problem

Abstract

The studies dealing with the problem of predicting scientific impacts in the scientific world mostly focus on predicting citation count of papers (PCCP). However, in the literature, only a little bit of research has been conducted on estimating the future influence of scientists individually. Estimating the impact of scientists individually is a worthwhile task for the following scientific research and cooperatives. From this point of view, a new supervised link prediction method is proposed to predict the citation count of scientists (PCCS). Many PCCP studies employ document-based attributes, such as titles, abstracts, and keywords of papers; institutions of scientists; impact factors of publishers; etc. and they do not take advantage of any topological features of complex networks formed with citations among papers. However, citation networks include valuable features for PCCP and PCCS. Therefore, we formulate the problem of PCCS as a link prediction problem in directed, weighted, and temporal citation networks. The proposed approach predicts not only links but also its weights. Our supervised link prediction method is tested on two citation networks in Experiment 1. The results of Experiment 1 confirm that our method achieves promising performances when considering prediction links with its weights are addressed for the first time in terms of link prediction in directed, weighted, and temporal networks. In Experiment 2, the performance of the proposed link prediction metric and five well-known link prediction metrics are compared in terms of prediction new links in complex networks. The results of Experiment 2 demonstrate that the proposed link prediction metric outperforms all baseline link prediction metrics.