统一图神经网络池化接口，让不同池化方法能轻松切换和比较。
Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
- 基于SRCL分解设计通用接口，标准化池化流程
- 提供20种层次化池化器与标准化输出格式
- 适合需要快速实验多种池化方法的研究者
Torch Geometric Pool（tgp）是基于PyTorch Geometric构建的图池化库。图池化方法在节点聚类方式、批处理处理、池化后返回内容以及是否暴露辅助损失等方面存在差异，导致方法难以比较或代码复用。tgp通过基于选择-归约-连接-提升（SRCL）分解的统一软件接口解决此问题。该库提供20种层次化池化器、标准化输出对象、独立读出模块、支持稠密池化器在批处理与非批处理模式下的运行，以及缓存与预粗化的工作流。项目以MIT许可证发布于GitHub和PyPI，附带完整文档、教程与示例。
原文摘要 · Abstract (English)
Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernodes, how they handle batches, what they return after pooling, and whether they expose auxiliary losses. These differences make it hard to compare methods or reuse the same model code across them. tgp addresses this problem with a common software interface based on the Select-Reduce-Connect-Lift (SRCL) decomposition. The library provides 20 hierarchical poolers, standardized output objects, standalone readout modules, support for dense poolers in batched and unbatched mode, and workflows for caching and pre-coarsening. It is released under the MIT license on GitHub and PyPI, with comprehensive documentation, tutorials, and examples.
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