  This paper proposes a novel model for predicting subgraphs in dynamic graphs,
an extension of traditional link prediction. This proposed end-to-end model
learns a mapping from the subgraph structures in the current snapshot to the
subgraph structures in the next snapshot directly, i.e., edge existence among
multiple nodes in the subgraph. A new mechanism named cross-attention with a
twin-tower module is designed to integrate node attribute information and
topology information collaboratively for learning subgraph evolution. We
compare our model with several state-of-the-art methods for subgraph prediction
and subgraph pattern prediction in multiple real-world homogeneous and
heterogeneous dynamic graphs, respectively. Experimental results demonstrate
that our model outperforms other models in these two tasks, with a gain
increase from 5.02% to 10.88%.
