  Scientific publications play a vital role in the career of a researcher.
However, some articles become more popular than others among the research
community and subsequently drive future research directions. One of the
indicative signs of popular articles is the number of citations an article
receives. The citation count, which is also the basis with various other
metrics, such as the journal impact factor score, the $h$-index, is an
essential measure for assessing a scientific paper's quality. In this work, we
proposed a Graph Convolutional Network (GCN) based framework for estimating
future research publication citations for both the short-term (1-year) and
long-term (for 5-years and 10-years) duration. We have tested our proposed
approach over the AMiner dataset, specifically on research articles from the
computer science domain, consisting of more than 0.8 million articles.
