We introduce Torch Geometric Pool (tgp), a library for hierarchical pooling in Graph Neural Networks. Built upon Pytorch Geometric, Torch Geometric Pool (tgp) provides a wide variety of pooling operators, unified under a consistent API and a modular design. The library emphasizes usability and extensibility, and includes features like precomputed pooling, which significantly accelerate training for a class of operators. In this paper, we present tgp's structure and present an extensive benchmark. The latter showcases the library's features and systematically compares the performance of the implemented graph-pooling methods in different downstream tasks. The results, showing that the choice of the optimal pooling operator depends on tasks and data at hand, support the need for a library that enables fast prototyping.

翻译：本文介绍Torch Geometric Pool（tgp），一个用于图神经网络层次化池化的库。该库基于PyTorch Geometric构建，在统一的API和模块化设计下提供了多样化的池化算子。tgp强调易用性和可扩展性，并包含预计算池化等特性，可显著加速特定类别算子的训练过程。本文阐述了tgp的架构，并进行了全面的基准测试。该测试展示了库的核心功能，并系统比较了所实现的图池化方法在不同下游任务中的性能。结果表明，最优池化算子的选择取决于具体任务和数据特性，这印证了开发支持快速原型设计的库的必要性。