PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network
This library addresses the problem of complex coding and inefficiency for researchers and practitioners working with HOGNNs, offering a user-friendly solution that is incremental in nature.
The authors tackled the lack of a standardized framework for High Order Graph Neural Networks (HOGNNs) by introducing PyTorch Geometric High Order (PyGHO), a library that provides a unified interface, resulting in up to 50% acceleration and a tenfold reduction in code needed for implementation.
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 \url{https://github.com/GraphPKU/PygHO}.