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A Supervised Learning Method for Prediction Citation Count of Scientists in Citation Networks

Predicting the future impacts of scientists and their publications is an important task for future scientific studies. Studies predicting scientific effects in the academia are mostly based on estimating citation numbers of publications. However, there are few studies focusing on predicting the impact of scientists. Determining the potential influence of scientists is useful for future scientific studies and organizations. In this respect, we propose a new approach for predicting future impacts of scientists by considering the number of citations that each scientist individually has. A great number of studies predicting citation count of publications use content based information such as title, abstract and keywords of publication, institution of scientists, journal impact factor of publisher, etc. and they don’t benefit from graph structures in citation networks. However, citations among scientific publications create more complex networks which have the potential for mining scientific impacts. So, we formulated the problem of predicting future citation counts of a given scientist, as a link prediction problem for directed, weighted and dynamic citation networks. The proposed link prediction method is able to predict links and its weights. Firstly, we introduce a dynamic metric based on increasing and decreasing trends during transitions time frames in citation networks. Then, we use the introduced dynamic proximity metric with some simple topological features in classifiers to predict citation count. Experiments are conducted on 12 sampled datasets which are extracted from two citation networks. The experimental results show that the proposed approach performs well when considering the problem of prediction links with its weights is firstly addressed in the context of link prediction problem in directed, weighted and dynamic networks.