Citation Count Prediction Based on Academic Network Features

Xin Ping Zhu · Zhijie Ban

20 citations0/0 files storedInternational Conference on Advanced Information Networking and Applications (AINA)

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scientometrics and bibliometrics research Complex Network Analysis Techniques Web visibility and informetrics

Abstract

Citation count is an important factor to measure the influence of academic publications. Identifying future citation count in advance can help scientists to find references and research area. There are many academic network features which are related to citation count. However, these features have not been completely explored in the existing studies. In this paper, we propose a citation count prediction model based on academic network features. Firstly, some important features are introduced and analyzed in detail. Then, we verify the importance of each feature and use a neural network model to select a set of optimal features. Finally, we present several machine learning methods and one multiple linear regression strategy to predict a paper's future citation. Experimental results on real datasets demonstrate that our model significantly outperforms the baseline method.

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References

2005An index to quantify an individual's scientific research output 1988Thirteen Ways to Look at the Correlation Coefficient 2002Journal Prestige, Publication Bias, and Other Characteristics Associated With Citation of Published Studies in Peer-Reviewed Journals 2006Earlier Web usage statistics as predictors of later citation impact 2009The most influential journals: Impact Factor and Eigenfactor 2008The Misused Impact Factor 2007Characteristics Associated with Citation Rate of the Medical Literature 2008Prediction of citation counts for clinical articles at two years using data available within three weeks of publication: retrospective cohort study

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