Prediction of emerging papers in nanocarbon materials-related research using a citation network

Nanocarbon materials made from graphite are used in diverse applications as semiconductors, fuel cells, optical devices, and structural materials because of their excellent mechanical, electrical, and thermal characteristics. Numerous papers are published annually in this area, and thus it is difficult to assess overall development in the field. Consequently, there is a need for approaches that predict advances from diverse and numerous sources of information. In this study, we used machine learning to examine papers on nanocarbon materials and related topics and to predict papers with emerging ideas that are expected to grow in popularity. We specifically predicted emerging papers that were ranked in the top 5% by number of citations. A total of 411,084 related papers were extracted from the Web of Science Core Collection (Thomson Reuters). A time-expanded network was produced from these data using citation links, and features of each paper were used as explanatory variables to build a prediction model. In this model, 9 of the top 10 papers from 2011 predicted to be emerging satisfied the conditions for emerging papers. These results suggest that the model can predict the direction of nanocarbon materials technology, which is of considerable value for private companies and research institutions.

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