  Citation prediction of scholarly papers is of great significance in guiding
funding allocations, recruitment decisions, and rewards. However, little is
known about how citation patterns evolve over time. By exploring the inherent
involution property in scholarly paper citation, we introduce the Paper
Potential Index (PPI) model based on four factors: inherent quality of
scholarly paper, scholarly paper impact decaying over time, early citations,
and early citers' impact. In addition, by analyzing factors that drive citation
growth, we propose a multi-feature model for impact prediction. Experimental
results demonstrate that the two models improve the accuracy in predicting
scholarly paper citations. Compared to the multi-feature model, the PPI model
yields superior predictive performance in terms of range-normalized RMSE. The
PPI model better interprets the changes in citation, without the need to adjust
parameters. Compared to the PPI model, the multi-feature model performs better
prediction in terms of Mean Absolute Percentage Error and Accuracy; however,
their predictive performance is more dependent on the parameter adjustment.
