  Patent documents in the patent database (PatDB) are crucial for research,
development, and innovation as they contain valuable technical information.
However, PatDB presents a multifaceted challenge compared to publicly available
preprocessed databases due to the intricate nature of the patent text and the
inherent sparsity within the patent citation network. Although patent text
analysis and citation analysis bring new opportunities to explore patent data
mining, no existing work exploits the complementation of them. To this end, we
propose a joint semantic-topological evolutionary graph learning approach
(PatSTEG) to model the formation dynamics of patent citation networks. More
specifically, we first create a real-world dataset of Chinese patents named
CNPat and leverage its patent texts and citations to construct a patent
citation network. Then, PatSTEG is modeled to study the evolutionary dynamics
of patent citation formation by considering the semantic and topological
information jointly. Extensive experiments are conducted on CNPat and public
datasets to prove the superiority of PatSTEG over other state-of-the-art
methods. All the results provide valuable references for patent literature
research and technical exploration.
