  Influence prediction plays a crucial role in the academic community. The
amount of scholars' influence determines whether their work will be accepted by
others. Most existing research focuses on predicting one paper's citation count
after a period or identifying the most influential papers among the massive
candidates, without concentrating on an individual paper's negative or positive
impact on its authors. Thus, this study aims to formulate the prediction
problem to identify whether one paper can increase scholars' influence or not,
which can provide feedback to the authors before they publish their papers.
First, we presented the self-adapted ACC (Average Annual Citation Counts)
metric to measure authors' impact yearly based on their annual published
papers, paper citation counts, and contributions in each paper. Then, we
proposed the RD-GAT (Reference-Depth Graph Attention Network) model to
integrate heterogeneous graph information from different depth of references by
assigning attention coefficients on them. Experiments on AMiner dataset
demonstrated that the proposed ACC metrics could represent the authors
influence effectively, and the RD-GAT model is more efficiently on the academic
citation network, and have stronger robustness against the overfitting problem
compared with the baseline models. By applying the framework in this work,
scholars can identify whether their papers can improve their influence in the
future.
