  GNN-based recommenders have excelled in modeling intricate user-item
interactions through multi-hop message passing. However, existing methods often
overlook the dynamic nature of evolving user-item interactions, which impedes
the adaption to changing user preferences and distribution shifts in newly
arriving data. Thus, their scalability and performances in real-world dynamic
environments are limited. In this study, we propose GraphPro, a framework that
incorporates parameter-efficient and dynamic graph pre-training with prompt
learning. This novel combination empowers GNNs to effectively capture both
long-term user preferences and short-term behavior dynamics, enabling the
delivery of accurate and timely recommendations. Our GraphPro framework
addresses the challenge of evolving user preferences by seamlessly integrating
a temporal prompt mechanism and a graph-structural prompt learning mechanism
into the pre-trained GNN model. The temporal prompt mechanism encodes time
information on user-item interaction, allowing the model to naturally capture
temporal context, while the graph-structural prompt learning mechanism enables
the transfer of pre-trained knowledge to adapt to behavior dynamics without the
need for continuous incremental training. We further bring in a dynamic
evaluation setting for recommendation to mimic real-world dynamic scenarios and
bridge the offline-online gap to a better level. Our extensive experiments
including a large-scale industrial deployment showcases the lightweight plug-in
scalability of our GraphPro when integrated with various state-of-the-art
recommenders, emphasizing the advantages of GraphPro in terms of effectiveness,
robustness and efficiency. The implementation details and source code of our
GraphPro are available in the repository at https://github.com/HKUDS/GraphPro
