Community Detection and Growth Potential Prediction from Patent Citation Networks
Asahi Hentona, Takeshi Sakumoto, Hugo Alberto Mendoza España, Hirofumi Nonaka, Shotaro Kataoka, Toru Hiraoka, Kensei Nakai, Elisa Claire Alemán Carreón, Masaharu Hirota
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The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.