Implicit graph neural networks have gained popularity in recent years as they
capture long-range dependencies while improving predictive performance in
static graphs. Despite the tussle between performance degradation due to the
oversmoothing of learned embeddings and long-range dependency being more
pronounced in dynamic graphs, as features are aggregated both across
neighborhood and time, no prior work has proposed an implicit graph neural
model in a dynamic setting. In this paper, we present Implicit Dynamic Graph
Neural Network (IDGNN) a novel implicit neural network for dynamic graphs which
is the first of its kind. A key characteristic of IDGNN is that it demonstrably
is well-posed, i.e., it is theoretically guaranteed to have a fixed-point
representation. We then demonstrate that the standard iterative algorithm often
used to train implicit models is computationally expensive in our dynamic
setting as it involves computing gradients, which themselves have to be
estimated in an iterative manner. To overcome this, we pose an equivalent
bilevel optimization problem and propose an efficient single-loop training
algorithm that avoids iterative computation by maintaining moving averages of
key components of the gradients. We conduct extensive experiments on real-world
datasets on both classification and regression tasks to demonstrate the
superiority of our approach over the state-of-the-art baselines. We also
demonstrate that our bi-level optimization framework maintains the performance
of the expensive iterative algorithm while obtaining up to \textbf{1600x}
speed-up.

Implicit Dynamic Graph Neural Network (IDGNN) 是一种针对动态图的新型隐式神经网络模型，通过提出一个等价的双层优化问题和一种高效的单循环训练算法来克服计算上的困难，以在分类和回归任务中展现出优于现有模型的性能，并在性能保持的同时获得高达 1600 倍的加速。