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Influence prediction plays a crucial role in the academic community. Theamount of scholars' influence determines whether their work will be accepted byothers. Most existing research focuses on predicting one paper's citation countafter a period or identifying the most influential papers among the massivecandidates, without concentrating on an individual paper's negative or positiveimpact on its authors. Thus, this study aims to formulate the predictionproblem 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 publishedpapers, paper citation counts, and contributions in each paper. Then, weproposed the RD-GAT (Reference-Depth Graph Attention Network) model tointegrate heterogeneous graph information from different depth of references byassigning attention coefficients on them. Experiments on AMiner datasetdemonstrated that the proposed ACC metrics could represent the authorsinfluence effectively, and the RD-GAT model is more efficiently on the academiccitation network, and have stronger robustness against the overfitting problemcompared with the baseline models. By applying the framework in this work,scholars can identify whether their papers can improve their influence in thefuture.

Fusion of the Power from Citations: Enhance your Influence by Integrating Information from References