The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks
(GNNs) is gradually attracting attention due to the low power consumption and
high efficiency in processing the non-Euclidean data represented by graphs.
However, as a common problem, dynamic graph representation learning faces
challenges such as high complexity and large memory overheads. Current work
often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary
features instead of continuous ones for efficient training, which would
overlooks graph structure information and leads to the loss of details during
propagation. Additionally, optimizing dynamic spiking models typically requires
propagation of information across time steps, which increases memory
requirements. To address these challenges, we present a framework named
\underline{Dy}namic \underline{S}p\underline{i}king \underline{G}raph
\underline{N}eural Networks (\method{}). To mitigate the information loss
problem, \method{} propagates early-layer information directly to the last
layer for information compensation. To accommodate the memory requirements, we
apply the implicit differentiation on the equilibrium state, which does not
rely on the exact reverse of the forward computation. While traditional
implicit differentiation methods are usually used for static situations,
\method{} extends it to the dynamic graph setting. Extensive experiments on
three large-scale real-world dynamic graph datasets validate the effectiveness
of \method{} on dynamic node classification tasks with lower computational
costs.

该研究论文提出了一种名为动态神经图网络（Dynamic Spiking Graph Neural Networks）的框架，用于解决动态图表示学习中的信息丢失和内存要求问题，通过将早期信息直接传递到最后一层以进行信息补偿，并在动态图环境中扩展了隐式微分以适应内存要求，并通过在三个大规模实际动态图数据集上的大量实验证明了其在动态节点分类任务上的有效性与较低的计算成本。