  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.
