Deep Graph Networks (DGNs) currently dominate the research landscape of
learning from graphs, due to their efficiency and ability to implement an
adaptive message-passing scheme between the nodes. However, DGNs are typically
limited in their ability to propagate and preserve long-term dependencies
between nodes, i.e., they suffer from the over-squashing phenomena. This
reduces their effectiveness, since predictive problems may require to capture
interactions at different, and possibly large, radii in order to be effectively
solved. In this work, we present Anti-Symmetric Deep Graph Networks (A-DGNs), a
framework for stable and non-dissipative DGN design, conceived through the lens
of ordinary differential equations. We give theoretical proof that our method
is stable and non-dissipative, leading to two key results: long-range
information between nodes is preserved, and no gradient vanishing or explosion
occurs in training. We empirically validate the proposed approach on several
graph benchmarks, showing that A-DGN yields to improved performance and enables
to learn effectively even when dozens of layers are used.

本文介绍了一种通过分析常微分方程而提出的抗对称深度图网络（A-DGNs）框架，该框架设计稳定而不耗散，能够有效地保存节点之间的长距离信息，并且不会发生梯度消失或爆炸的现象。该方法在多个图像基准测试中均获得了改进的性能，即使使用大量的层数也能有效学习。