  Citation count prediction is the task of predicting the number of citations a
paper has gained after a period of time. Prior work viewed this as a static
prediction task. As papers and their citations evolve over time, considering
the dynamics of the number of citations a paper will receive would seem
logical. Here, we introduce the task of sequence citation prediction. The goal
is to accurately predict the trajectory of the number of citations a scholarly
work receives over time. We propose to view papers as a structured network of
citations, allowing us to use topological information as a learning signal.
Additionally, we learn how this dynamic citation network changes over time and
the impact of paper meta-data such as authors, venues and abstracts. To
approach the new task, we derive a dynamic citation network from Semantic
Scholar spanning over 42 years. We present a model which exploits topological
and temporal information using graph convolution networks paired with sequence
prediction, and compare it against multiple baselines, testing the importance
of topological and temporal information and analyzing model performance. Our
experiments show that leveraging both the temporal and topological information
greatly increases the performance of predicting citation counts over time.
