Many real-world problems can be represented as graph-based learning problems.
In this paper, we propose a novel framework for learning spatial and
attentional convolution neural networks on arbitrary graphs. Different from
previous convolutional neural networks on graphs, we first design a
motif-matching guided subgraph normalization method to capture neighborhood
information. Then we implement subgraph-level self-attentional layers to learn
different importances from different subgraphs to solve graph classification
problems. Analogous to image-based attentional convolution networks that
operate on locally connected and weighted regions of the input, we also extend
graph normalization from one-dimensional node sequence to two-dimensional node
grid by leveraging motif-matching, and design self-attentional layers without
requiring any kinds of cost depending on prior knowledge of the graph
structure. Our results on both bioinformatics and social network datasets show
that we can significantly improve graph classification benchmarks over
traditional graph kernel and existing deep models.

本文提出了一种新颖的框架，可以在任意图上学习空间和关注卷积神经网络，通过子图归一化和自我关注层，可以显著提高传统图内核和现有深度模型的图分类基准。