Measuring the impact of novelty, bibliometric, and academic-network factors on citation count using a neural network

作者：

Highlights：

• The paper generated 24 factors from Library and Information science, Computer science, Nuclear Science and History articles.

• Factors were grouped into novelty, bibliometric, academic-network categories and influences on citations were identified.

• We measured factor influences using the weight product of connecting neurons of a feedforward artificial neural network.

• The influence of multiple factors in the novelty category on citation counts is higher than other categories.

摘要

•The paper generated 24 factors from Library and Information science, Computer science, Nuclear Science and History articles.•Factors were grouped into novelty, bibliometric, academic-network categories and influences on citations were identified.•We measured factor influences using the weight product of connecting neurons of a feedforward artificial neural network.•The influence of multiple factors in the novelty category on citation counts is higher than other categories.

论文关键词：Novelty,Bibliometric,Academic network,Paper citation count,Neural network model

论文评审过程：Received 7 August 2020, Revised 25 January 2021, Accepted 29 January 2021, Available online 12 February 2021, Version of Record 12 February 2021.

论文官网地址：https://doi.org/10.1016/j.joi.2021.101140