论文标题
LookHops：图形分类的光多阶卷积和合并
LookHops: light multi-order convolution and pooling for graph classification
论文作者
论文摘要
卷积和合并是学习图形分类的层次结构表示的关键操作，其中表达式$ k $ - 订单（$ k> 1 $）方法需要更多的计算成本，从而限制了更多的应用程序。在本文中，我们调查了通过邻里信息增益选择$ K $的策略，并提出了$ k $ order订单卷积和汇总，在改善性能的同时需要更少的参数。通过六个图形分类基准进行的全面和公平的实验显示：1）绩效改进与$ k $ - 订单信息增益一致。 2）拟议的卷积需要更少的参数，同时提供竞争结果。 3）拟议的合并在效率和性能方面优于SOTA算法。
Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive $k$-order($k>1$) method requires more computation cost, limiting the further applications. In this paper, we investigate the strategy of selecting $k$ via neighborhood information gain and propose light $k$-order convolution and pooling requiring fewer parameters while improving the performance. Comprehensive and fair experiments through six graph classification benchmarks show: 1) the performance improvement is consistent to the $k$-order information gain. 2) the proposed convolution requires fewer parameters while providing competitive results. 3) the proposed pooling outperforms SOTA algorithms in terms of efficiency and performance.
