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The scoring of patents is useful for technology management analysis.Therefore, a necessity of developing citation network clustering and predictionof future citations for practical patent scoring arises. In this paper, wepropose a community detection method using the Node2vec. And in order toanalyze growth potential we compare three ''time series analysis methods'', theLong Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results ofour experiments, we could find common technical points from those clusters byNode2vec. Furthermore, we found that the prediction accuracy of the ARIMA modelwas higher than that of other models.

Community Detection and Growth Potential Prediction from Patent Citation Networks