论文标题
结构熵指导图层次合并
Structural Entropy Guided Graph Hierarchical Pooling
论文作者
论文摘要
在非欧几里得空间上卷积成功之后，在各种有关图形的任务上也验证了相应的池化方法。但是，由于固定的压缩配额和逐步合并设计，这些分层池化方法仍然遭受局部结构损害和次优问题的困扰。在这项工作的启发下，我们提出了一种层次的合并方法，即SEP，以解决这两个问题。具体而言，在不分配特定层的压缩配额的情况下，全局优化算法旨在生成一次群集分配矩阵以一次汇总。然后，我们提出了在环和网格合成图重建中先前方法中局部结构损害的例证。除SEP外，我们还将分别设计两个分类模型，分别用于图形分类和节点分类。结果表明，SEP在图形分类基准上优于最先进的图形合并方法，并在节点分类上获得了卓越的性能。
Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of the fixed compression quota and stepwise pooling design, these hierarchical pooling methods still suffer from local structure damage and suboptimal problem. In this work, inspired by structural entropy, we propose a hierarchical pooling approach, SEP, to tackle the two issues. Specifically, without assigning the layer-specific compression quota, a global optimization algorithm is designed to generate the cluster assignment matrices for pooling at once. Then, we present an illustration of the local structure damage from previous methods in the reconstruction of ring and grid synthetic graphs. In addition to SEP, we further design two classification models, SEP-G and SEP-N for graph classification and node classification, respectively. The results show that SEP outperforms state-of-the-art graph pooling methods on graph classification benchmarks and obtains superior performance on node classifications.
