6 research outputs found
Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks
Scoring patent documents is very useful for technology management. However,
conventional methods are based on static models and, thus, do not reflect the
growth potential of the technology cluster of the patent. Because even if the
cluster of a patent has no hope of growing, we recognize the patent is
important if PageRank or other ranking score is high. Therefore, there arises a
necessity of developing citation network clustering and prediction of future
citations. In our research, clustering of patent citation networks by
Stochastic Block Model was done with the aim of enabling corporate managers and
investors to evaluate the scale and life cycle of technology. As a result, we
confirmed nested SBM is appropriate for graph clustering of patent citation
networks. Also, a high MAPE value was obtained and the direction accuracy
achieved a value greater than 50% when predicting growth potential for each
cluster by using LSTM.Comment: arXiv admin note: substantial text overlap with arXiv:1904.1204
Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"
Affected by urbanization, centralization and the decrease of overall
population, Japan has been making efforts to revitalize the rural areas across
the country. One particular effort is to increase tourism to these rural areas
via regional branding, using local farm products as tourist attractions across
Japan. Particularly, a program subsidized by the government called Michinoeki,
which stands for 'roadside station', was created 20 years ago and it strives to
provide a safe and comfortable space for cultural interaction between road
travelers and the local community, as well as offering refreshment, and
relevant information to travelers. However, despite its importance in the
revitalization of the Japanese economy, studies with newer technologies and
methodologies are lacking. Using sales data from establishments in the Kyushu
area of Japan, we used Support Vector to classify content from Twitter into
relevant topics and studied their causal relationship to the sales for each
establishment using LiNGAM, a linear non-gaussian acyclic model built for
causal structure analysis, to perform an improved market analysis considering
more than just correlation. Under the hypotheses stated by the LiNGAM model, we
discovered a positive causal relationship between the number of tweets
mentioning those establishments, specially mentioning deserts, a need for
better access and traf^ic options, and a potentially untapped customer base in
motorcycle biker groups
Community Detection and Growth Potential Prediction from Patent Citation Networks
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.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author