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Temporal citation network-based feature extraction for cited count prediction
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Publication Year
2018-01-01
Journal
Lecture Notes in Electrical Engineering
Publisher
Springer Verlag
Citation
Lecture Notes in Electrical Engineering, Vol.425, pp.380-388
Mesh Keyword
Citation networksCoefficient of determinationNetwork centralitiesPerformance measurePrediction problemTime transition
All Science Classification Codes (ASJC)
Industrial and Manufacturing Engineering
Abstract
The academic data that keeps the advanced knowledge of mankind continues to increase. Accordingly, researchers have been actively conducted research to find important ones among the academic data. This study presents new features for citation count prediction problem. The new features are derived from the network centrality analysis with time transition variance and are compared with the existing author, venue, and content features to verify their excellence. We use coefficient of determination (R2) as a performance measure, and it has been confirmed that our proposed features are more useful for predicting the citation count than the existing features. Along with presenting new features, we have also attempted time-series analysis to observe whether the features used in the prediction change their influence with time. Thus, we have found that the influence of features does not change much over time.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36340
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85022195461&origin=inward
DOI
https://doi.org/2-s2.0-85022195461
Journal URL
http://www.springer.com/series/7818
Type
Conference
Funding
This research was supported by the MISP (Ministry of Science, ICT & Future Planning), Korea, under the National Program for Excellence in SW) supervised by the IITP (Institute for Information & communications Technology Promotion) (R22151610020001002)
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Sohn, Kyung-Ah Image
Sohn, Kyung-Ah손경아
Department of Software and Computer Engineering
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