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)

翻译：摘要：论文的潜在影响力通常通过其未来获得的被引次数来衡量。然而，大多数常用模型往往低估了新发表论文随时间推移的影响力，且未能将引文网络的动态演化特征纳入图结构表示中。本研究从年度视角出发，为目标论文构建层级异质图，该图结构可动态记录目标论文所处科学语境信息随时间的变化。随后，我们提出新型图神经网络模型——层级异质对比图学习模型（H2CGL），以融合引文网络的异质性与动态性。H2CGL分别聚合每一年度的异质信息，优先关注高被引论文以及参考文献、施引文献与目标论文之间的关系，并通过加权图同构网络（Weighted GIN）捕捉多年间异质子图间的动态演变规律。此外，模型利用对比学习增强图表示对潜在被引的敏感性：特别地，将与被引量差距较大的目标论文具有共被引或共引关系的论文作为困难负样本，而随机丢弃低被引论文可生成正样本。在两个学术数据集上的大量实验表明，H2CGL在历史论文与新近论文预测任务中均显著优于基线方法。附加分析进一步验证了所提出模块的有效性。相关代码与配置已开源至GitHub（https://github.com/ECNU-Text-Computing/H2CGL）。