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The potential impact of a paper is often quantified by how many citations itwill receive. However, most commonly used models may underestimate theinfluence of newly published papers over time, and fail to encapsulate thisdynamics of citation network into the graph. In this study, we constructhierarchical and heterogeneous graphs for target papers with an annualperspective. The constructed graphs can record the annual dynamics of targetpapers' scientific context information. Then, a novel graph neural network,Hierarchical and Heterogeneous Contrastive Graph Learning Model (H2CGL), isproposed to incorporate heterogeneity and dynamics of the citation network.H2CGL separately aggregates the heterogeneous information for each year andprioritizes the highly-cited papers and relationships among references,citations, and the target paper. It then employs a weighted GIN to capturedynamics between heterogeneous subgraphs over years. Moreover, it leveragescontrastive learning to make the graph representations more sensitive topotential citations. Particularly, co-cited or co-citing papers of the targetpaper with large citation gap are taken as hard negative samples, whilerandomly dropping low-cited papers could generate positive samples. Extensiveexperimental results on two scholarly datasets demonstrate that the proposedH2CGL significantly outperforms a series of baseline approaches for bothpreviously and freshly published papers. Additional analyses highlight thesignificance of the proposed modules. Our codes and settings have been releasedon Github (https://github.com/ECNU-Text-Computing/H2CGL)

H2CGL: Modeling Dynamics of Citation Network for Impact Prediction