论文标题
图形神经网络的高阶聚类和集合
Higher-order Clustering and Pooling for Graph Neural Networks
论文作者
论文摘要
图形神经网络在众多图形分类任务上实现了最新的性能，尤其是由于合并运算符，这些操作员汇总了学习的节点嵌入在最终的图表表示中。但是，他们不仅对显示出随机池的PAR性能的最新工作对它们进行了质疑，而且还忽略了完全高阶的连接模式。为了解决此问题，我们提出了Hoscpool，这是一个基于聚类的图形池操作员，该操作员从层次上捕获高阶信息，从而导致更丰富的图表表示。实际上，我们通过在目标函数中最大程度地减少基序谱聚类的松弛公式来最大程度地减少概率的群集分配矩阵，然后将其扩展到合并操作员。我们在图形分类任务上评估了Hoscpool及其在具有地面社区结构的图形上的聚类组件，从而实现了最佳性能。最后，我们对合并运营商的内部功能进行了深入的经验分析。
Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks, especially due to pooling operators, which aggregate learned node embeddings hierarchically into a final graph representation. However, they are not only questioned by recent work showing on par performance with random pooling, but also ignore completely higher-order connectivity patterns. To tackle this issue, we propose HoscPool, a clustering-based graph pooling operator that captures higher-order information hierarchically, leading to richer graph representations. In fact, we learn a probabilistic cluster assignment matrix end-to-end by minimising relaxed formulations of motif spectral clustering in our objective function, and we then extend it to a pooling operator. We evaluate HoscPool on graph classification tasks and its clustering component on graphs with ground-truth community structure, achieving best performance. Lastly, we provide a deep empirical analysis of pooling operators' inner functioning.
