提出一种自适应聚类的图池化方法，兼顾效率与结构保持。
SpaPool: Soft Partition Assignment Pooling for__Graph Neural Networks
- 通过软分配动态聚类节点，融合稠密与稀疏方法优势。
- 在多个数据集上表现媲美现有方法，小规模图效果更优。
- 适合需要高效图处理的场景，如小图分析或资源受限环境。
本文提出SpaPool，一种新型图神经网络池化方法，结合稠密与稀疏技术的优势。SpaPool根据图结构自适应地将节点分组为若干簇，旨在维持图的结构完整性的同时高效缩减图规模。在多个数据集上的实验结果表明，SpaPool性能可与现有池化方法相媲美，尤其在小规模图上表现突出。这使其成为需要高效且有效图处理应用的有前景方案。
原文摘要 · Abstract (English)
This paper introduces SpaPool, a novel pooling method that combines the strengths of both dense and sparse techniques for a graph neural network. SpaPool groups vertices into an adaptive number of clusters, leveraging the benefits of both dense and sparse approaches. It aims to maintain the structural integrity of the graph while reducing its size efficiently. Experimental results on several datasets demonstrate that SpaPool achieves competitive performance compared to existing pooling techniques and excels particularly on small-scale graphs. This makes SpaPool a promising method for applications requiring efficient and effective graph processing.
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