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.

DyExpert 是一个动态图模型，用于跨领域链接预测，并通过显式建模历史演化过程来学习特定下游图的演化模式，从而进行特定模式的链接预测。在充分训练过的动态图上，DyExpert 在八个未训练的图上进行广泛实验，表现出了跨领域链接预测方面的最新性能。与同样设置下的先进基准相比，DyExpert 在八个图上的平均精度提升了 11.40％，并在六个未训练的图上超过了八个先进基准的全监督性能。

跨领域动态链接预测的一种图模型

One Graph Model for Cross-domain Dynamic Link Prediction

We propose a modeling framework for growing multiplexes where a node can
belong to different networks. We define new measures for multiplexes and we
identify a number of relevant ingredients for modeling their evolution such as
the coupling between the different layers and the arrival time distribution of
nodes. The topology of the multiplex changes significantly in the different
cases under consideration, with effects of the arrival time of nodes on the
degree distribution, average shortest paths and interdependence.

提出一种多重网络的建模框架，其中节点可以属于不同的网络，并定义了新的多重网络的度量标准以及模拟它们演化的相关影响因素，如不同层之间的耦合和节点到达时间分布等。节点的到达时间对于网络的拓扑结构的演变产生了显著影响，尤其是在度分布、平均最短路径和相互依赖性方面。