title: Science communication. Response to Comment on "Quantifying long-term scientific impact".
abstract: 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.
authors.affiliation: IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA.
authors.forename: Dashun
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Physics, University of Miami, Coral Gables, FL 33146, USA.
authors.forename: Chaoming
authors.initials: C
authors.lastname: Song
authors.affiliation: Center for Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, MA 02115, USA. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Hua-Wei
authors.initials: HW
authors.lastname: Shen
authors.affiliation: Center for Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, MA 02115, USA. alb@neu.edu.
authors.forename: Albert-László
authors.initials: AL
authors.lastname: Barabási
mesh_terms: D055811:Journal Impact Factor; D008962:Models, Theoretical