We introduce PyTorch Geometric High Order (PyGHO), a library for High Order
Graph Neural Networks (HOGNNs) that extends PyTorch Geometric (PyG). Unlike
ordinary Message Passing Neural Networks (MPNNs) that exchange messages between
nodes, HOGNNs, encompassing subgraph GNNs and k-WL GNNs, encode node tuples, a
method previously lacking a standardized framework and often requiring complex
coding. PyGHO's main objective is to provide an unified and user-friendly
interface for various HOGNNs. It accomplishes this through streamlined data
structures for node tuples, comprehensive data processing utilities, and a
flexible suite of operators for high-order GNN methodologies. In this work, we
present a detailed in-depth of PyGHO and compare HOGNNs implemented with PyGHO
with their official implementation on real-world tasks. PyGHO achieves up to
$50\%$ acceleration and reduces the code needed for implementation by an order
of magnitude. Our library is available at
https://github.com/GraphPKU/PygHO.

PyTorch Geometric High Order (PyGHO) 是一个用于高阶图神经网络（HOGNNs）的库，扩展了 PyTorch Geometric（PyG）。该库通过简化的节点元组数据结构、全面的数据处理工具和高阶 GNN 方法的灵活运算符，提供了一个统一且用户友好的接口。本研究详细介绍了 PyGHO，并将使用 PyGHO 实现的 HOGNNs 与官方实现在真实任务上进行比较。PyGHO 可以实现高达 50% 的加速，并将实现所需的代码量减少一个数量级。