  Many real-world relational systems, such as social networks and biological
systems, contain dynamic interactions. When learning dynamic graph
representation, it is essential to employ sequential temporal information and
geometric structure. Mainstream work achieves topological embedding via message
passing networks (e.g., GCN, GAT). The temporal evolution, on the other hand,
is conventionally expressed via memory units (e.g., LSTM or GRU) that possess
convenient information filtration in a gate mechanism. Though, such a design
prevents large-scale input sequence due to the over-complicated encoding. This
work learns from the philosophy of self-attention and proposes an efficient
spectral-based neural unit that employs informative long-range temporal
interaction. The developed spectral window unit (SWINIT) model predicts
scalable dynamic graphs with assured efficiency. The architecture is assembled
with a few simple effective computational blocks that constitute randomized
SVD, MLP, and graph Framelet convolution. The SVD plus MLP module encodes the
long-short-term feature evolution of the dynamic graph events. A fast framelet
graph transform in the framelet convolution embeds the structural dynamics.
Both strategies enhance the model's ability on scalable analysis. In
particular, the iterative SVD approximation shrinks the computational
complexity of attention to O(Nd\log(d)) for the dynamic graph with N edges and
d edge features, and the multiscale transform of framelet convolution allows
sufficient scalability in the network training. Our SWINIT achieves
state-of-the-art performance on a variety of online continuous-time dynamic
graph learning tasks, while compared to baseline methods, the number of its
learnable parameters reduces by up to seven times.
