Predicting publication productivity for authors: Shallow or deep architecture?

by   Wumei Du, et al.

Academic administrators and funding agencies must predict the publication productivity of research groups and individuals to assess authors' abilities. However, such prediction remains an elusive task due to the randomness of individual research and the diversity of authors' productivity patterns. We applied two kinds of approaches to this prediction task: deep neural network learning and model-based approaches. We found that a neural network cannot give a good long-term prediction for groups, while the model-based approaches cannot provide short-term predictions for individuals. We proposed a model that integrates the advantages of both data-driven and model-based approaches, and the effectiveness of this method was validated by applying it to a high-quality dblp dataset, demonstrating that the proposed model outperforms the tested data-driven and model-based approaches.



page 1

page 2

page 3

page 4


Predicting publication productivity for researchers: A latent variable model

This study provided a model for the publication dynamics of researchers,...

Predicting publication productivity for researchers: a piecewise Poisson model

Predicting the scientific productivity of researchers is a basic task fo...

AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks

Data driven modeling based approaches have recently gained a lot of atte...

Model-Based and Data-Driven Strategies in Medical Image Computing

Model-based approaches for image reconstruction, analysis and interpreta...

Data-Driven Multi-step Demand Prediction for Ride-hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most d...

Collaboration Drives Individual Productivity

How does the number of collaborators affect individual productivity? Res...

Predicting the number of coauthors for researchers: A learning model

Predicting the number of coauthors for researchers contributes to unders...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.