论文标题
ACMP：Allen-Cahn消息传递，具有吸引力和排斥力的图形神经网络
ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks
论文作者
论文摘要
神经消息传递是一个基本的特征提取单元，用于图形结构化数据，考虑了从一层到另一层网络传播中相邻的节点特征。我们通过相互作用的粒子系统与具有吸引力和排斥力的相互作用粒子系统以及相位过渡建模时产生的艾伦 - 卡恩力对此进行建模。系统的动力学是一个反应扩散过程，可以将颗粒分开而不会爆炸。这诱导了图形神经网络的Allen-CAHN消息传递（ACMP），其中粒子系统解决方案的数值迭代构成了消息传播。具有简单实现的ACMP，具有神经ode求解器可以推动网络深度高达一百层，而理论上证明了严格的dirichlet能量下限。因此，它提供了GNNS的深层模型，规避了GNN过度厚度的常见问题。具有ACMP的GNN在同粒细胞和异性数据集上实现了现实世界节点分类任务的最先进性能。代码可在https://github.com/ykiiiiii/acmp上找到。
Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next. We model such process by an interacting particle system with attractive and repulsive forces and the Allen-Cahn force arising in the modeling of phase transition. The dynamics of the system is a reaction-diffusion process which can separate particles without blowing up. This induces an Allen-Cahn message passing (ACMP) for graph neural networks where the numerical iteration for the particle system solution constitutes the message passing propagation. ACMP which has a simple implementation with a neural ODE solver can propel the network depth up to one hundred of layers with theoretically proven strictly positive lower bound of the Dirichlet energy. It thus provides a deep model of GNNs circumventing the common GNN problem of oversmoothing. GNNs with ACMP achieve state of the art performance for real-world node classification tasks on both homophilic and heterophilic datasets. Codes are available at https://github.com/ykiiiiii/ACMP.
