论文标题
用于图形结构数据的卷积内核网络
Convolutional Kernel Networks for Graph-Structured Data
论文作者
论文摘要
我们介绍了一个多层图内核家族，并在图形卷积神经网络和内核方法之间建立了新的联系。我们的方法通过将图表示为内核特征映射序列，将卷积内核网络推广到图形结构化数据，其中每个节点都带有有关局部图形子结构的信息。一方面，内核的观点提供了无监督，表达且易于调查的数据表示，当可用的样本有限时，这很有用。另一方面，我们的模型也可以在大规模数据的端到端进行训练，从而导致新类型的图形卷积神经网络。我们表明，我们的方法在几个图形分类基准上实现了竞争性能，同时提供了简单的模型解释。我们的代码可在https://github.com/claying/gckn上免费获得。
We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional kernel networks to graph-structured data, by representing graphs as a sequence of kernel feature maps, where each node carries information about local graph substructures. On the one hand, the kernel point of view offers an unsupervised, expressive, and easy-to-regularize data representation, which is useful when limited samples are available. On the other hand, our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks. We show that our method achieves competitive performance on several graph classification benchmarks, while offering simple model interpretation. Our code is freely available at https://github.com/claying/GCKN.
