Community Detection and Growth Potential Prediction from Patent Citation Networks
This work addresses technology management analysis by providing incremental improvements in patent scoring through clustering and prediction methods.
The paper tackled the problem of patent scoring by developing a community detection method using Node2vec and comparing time series models (LSTM, ARIMA, Hawkes Process) to predict future citations, finding that ARIMA had higher prediction accuracy.
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.