Community Detection and Growth Potential Prediction from Patent Citation Networks

TL;DR

This paper introduces a community detection approach using Node2vec for patent citation networks and compares three time series models to predict future citations, finding ARIMA to be most accurate.

Contribution

It presents a novel combination of Node2vec for clustering patents and evaluates multiple models for growth prediction, highlighting ARIMA's superior performance.

Findings

Node2vec effectively identifies common features in patent clusters.

ARIMA outperforms LSTM and Hawkes in citation prediction accuracy.

Community detection aids in understanding patent technology groups.

Abstract

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.