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