Prompt Learning on Temporal Interaction Graphs

Understand

Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems.

• To facilitate representation learning on TIGs, researchers have proposed a series of TIG models.
• However, these models are still facing two tough gaps between the pre-training and downstream predictions in their ``pre-train, predict'' training paradigm.
• First, the temporal discrepancy between the pre-training and inference data severely undermines the models' applicability in distant future predictions on the dynamically evolving data.