With the advancement of science and technology, the number of academic papers published in the world each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we all know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the evaluation standard for decision-making such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very important. The most common standard for measuring academic papers is the number of citation counts of papers, because this indicator is widely used in the evaluation of scientific publications, and it also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. This paper proposes an end-to-end deep learning network, DeepCCP, which combines the effect of information cascade and looks at the citation counts prediction problem from the perspective of information cascade prediction. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and the output is the citation counts of the corresponding paper after a period of time. DeepCCP only uses the structure and temporal information of the citation network, and does not require other additional information, but it can still achieve outstanding performance. According to experiments on 6 real data sets, DeepCCP is superior to the state-of-the-art methods in terms of the accuracy of citation count prediction.

中文翻译：

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利用引文网络结构预测引文数：一种深度学习方法

随着科学技术的进步，全球每年发表的学术论文数量几乎呈指数级增长。大量的研究论文在凸显科技繁荣的同时，也带来了一些问题。众所周知，学术论文是学者研究成果最直观的体现，可以反映研究人员的水平。也是资金分配等决策的评价标准。因此，如何衡量一篇学术论文的质量非常重要。衡量学术论文最常用的标准是论文的引用次数，因为这个指标被广泛应用于科学出版物的评价中，它也是许多其他指标（如 h 指数）的基础。因此，能够准确预测学术论文的被引次数非常重要。本文提出了一个端到端的深度学习网络DeepCCP，它结合了信息级联的作用，从信息级联预测的角度来看待引用计数预测问题。DeepCCP 直接将论文前期形成的引文网络作为输入，输出一段时间后对应论文的引用次数。DeepCCP 仅使用引文网络的结构和时间信息，不需要其他附加信息，但仍能取得出色的性能。根据在 6 个真实数据集上的实验，