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。

基于模 motif 的注意力机制的图卷称神经网络

Graph Convolutional Neural Networks via Motif-based Attention

We present graph attention networks (GATs), novel neural network
architectures that operate on graph-structured data, leveraging masked
self-attentional layers to address the shortcomings of prior methods based on
graph convolutions or their approximations. By stacking layers in which nodes
are able to attend over their neighborhoods' features, we enable (implicitly)
specifying different weights to different nodes in a neighborhood, without
requiring any kind of costly matrix operation (such as inversion) or depending
on knowing the graph structure upfront. In this way, we address several key
challenges of spectral-based graph neural networks simultaneously, and make our
model readily applicable to inductive as well as transductive problems. Our GAT
models have achieved or matched state-of-the-art results across four
established transductive and inductive graph benchmarks: the Cora, Citeseer and
Pubmed citation network datasets, as well as a protein-protein interaction
dataset (wherein test graphs remain unseen during training).

本文提出新颖的神经网络结构图注意力网络（GATs），利用掩码的自我关注层处理图结构化数据的缺点，有效提出解决谱学派图神经网络中的几个关键挑战的方法，并在四个数据集上取得了最先进的结果。