IEICE TRANSACTIONS on Information
Citation Count Prediction Based on Neural Hawkes Model
Summary :
With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.11 pp.2379-2388
- Publication Date
- 2020/11/01
- Publicized
- 2020/08/03
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7051
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
Authors
Lisha LIU
Hangzhou Dianzi University,University of Yamanashi
Dongjin YU
Hangzhou Dianzi University
Dongjing WANG
Hangzhou Dianzi University
Fumiyo FUKUMOTO
University of Yamanashi
Keyword
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Lisha LIU, Dongjin YU, Dongjing WANG, Fumiyo FUKUMOTO, "Citation Count Prediction Based on Neural Hawkes Model" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 11, pp. 2379-2388, November 2020, doi: 10.1587/transinf.2020EDP7051.
Abstract: With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7051/_p
Copy
@ARTICLE{e103-d_11_2379,
author={Lisha LIU, Dongjin YU, Dongjing WANG, Fumiyo FUKUMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Citation Count Prediction Based on Neural Hawkes Model},
year={2020},
volume={E103-D},
number={11},
pages={2379-2388},
abstract={With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.},
keywords={},
doi={10.1587/transinf.2020EDP7051},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Citation Count Prediction Based on Neural Hawkes Model
T2 - IEICE TRANSACTIONS on Information
SP - 2379
EP - 2388
AU - Lisha LIU
AU - Dongjin YU
AU - Dongjing WANG
AU - Fumiyo FUKUMOTO
PY - 2020
DO - 10.1587/transinf.2020EDP7051
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2020
AB - With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.
ER -
English
