Response to Comment on ' ' Quantifying long - term scientific impact ' '
نویسندگان
چکیده
Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the proposed naïve approach.
منابع مشابه
Comment on "Quantifying Long-term Scientific Impact"
The paper comments on “Quantifying long-term scientific impact”. It indicates that there is a mistake of [D. S. Wang, C. Song, A. L. Barabási. Quantifying long-term scientific impact, Science, 342, 127 (2013)].
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Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large m (>5) and l (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citati...
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Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the ...
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