  Predicting the impact of publications in science and technology has become an
important research area, which is useful in various real world scenarios such
as technology investment, research direction selection, and technology
policymaking. Citation trajectory prediction is one of the most popular tasks
in this area. Existing approaches mainly rely on mining temporal and graph data
from academic articles. Some recent methods are capable of handling cold-start
prediction by aggregating metadata features of new publications. However, the
implicit factors causing citations and the richer information from handling
temporal and attribute features still need to be explored. In this paper, we
propose CTPIR, a new citation trajectory prediction framework that is able to
represent the influence (the momentum of citation) of either new or existing
publications using the history information of all their attributes. Our
framework is composed of three modules: difference-preserved graph embedding,
fine-grained influence representation, and learning-based trajectory
calculation. To test the effectiveness of our framework in more situations, we
collect and construct a new temporal knowledge graph dataset from the real
world, named AIPatent, which stems from global patents in the field of
artificial intelligence. Experiments are conducted on both the APS academic
dataset and our contributed AIPatent dataset. The results demonstrate the
strengths of our approach in the citation trajectory prediction task.
