Community Detection and Growth Potential Prediction from Patent Citation Networks
Information Retrieval
2019-04-30 v1 Social and Information Networks
Physics and Society
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.
Keywords
Cite
@article{arxiv.1904.12040,
title = {Community Detection and Growth Potential Prediction from Patent Citation Networks},
author = {Asahi Hentona and Takeshi Sakumoto and Hugo Alberto Mendoza España and Hirofumi Nonaka and Shotaro Kataoka and Toru Hiraoka and Kensei Nakai and Elisa Claire Alemán Carreón and Masaharu Hirota},
journal= {arXiv preprint arXiv:1904.12040},
year = {2019}
}
Comments
arXiv admin note: text overlap with arXiv:1607.00653 by other authors