  This work proposes DyExpert, a dynamic graph model for cross-domain link
prediction. It can explicitly model historical evolving processes to learn the
evolution pattern of a specific downstream graph and subsequently make
pattern-specific link predictions. DyExpert adopts a decode-only transformer
and is capable of efficiently parallel training and inference by
\textit{conditioned link generation} that integrates both evolution modeling
and link prediction. DyExpert is trained by extensive dynamic graphs across
diverse domains, comprising 6M dynamic edges. Extensive experiments on eight
untrained graphs demonstrate that DyExpert achieves state-of-the-art
performance in cross-domain link prediction. Compared to the advanced baseline
under the same setting, DyExpert achieves an average of 11.40% improvement
Average Precision across eight graphs. More impressive, it surpasses the fully
supervised performance of 8 advanced baselines on 6 untrained graphs.
