Investigating graph feature learning becomes essentially important with the
emergence of graph data in many real-world applications. Several graph neural
network approaches are proposed for node feature learning and they generally
follow a neighboring information aggregation scheme to learn node features.
While great performance has been achieved, the weights learning for different
neighboring nodes is still less explored. In this work, we propose a novel
graph network layer, known as Node2Seq, to learn node embeddings with
explicitly trainable weights for different neighboring nodes. For a target
node, our method sorts its neighboring nodes via attention mechanism and then
employs 1D convolutional neural networks (CNNs) to enable explicit weights for
information aggregation. In addition, we propose to incorporate non-local
information for feature learning in an adaptive manner based on the attention
scores. Experimental results demonstrate the effectiveness of our proposed
Node2Seq layer and show that the proposed adaptively non-local information
learning can improve the performance of feature learning.

本文提出了一种名为 Node2Seq 的新型图网络层，通过注意力机制和一维卷积神经网络实现了对不同相邻节点信息聚合的显示可训练权重，并提出了通过注意力分数以自适应方式融合非局部信息进行特征学习的方法，实验证明该方法提高了特征学习的性能。

Node2Seq: 用于图神经网络中可训练卷积的方法

Node2Seq: Towards Trainable Convolutions in Graph Neural Networks

During the last decade, Convolutional Neural Networks (CNNs) have become the
de facto standard for various Computer Vision and Machine Learning operations.
CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating
convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and
millions of parameters have the ability to learn complex objects and patterns
providing that they can be trained on a massive size visual database with
ground-truth labels. With a proper training, this unique ability makes them the
primary tool for various engineering applications for 2D signals such as images
and video frames. Yet, this may not be a viable option in numerous applications
over 1D signals especially when the training data is scarce or
application-specific. To address this issue, 1D CNNs have recently been
proposed and immediately achieved the state-of-the-art performance levels in
several applications such as personalized biomedical data classification and
early diagnosis, structural health monitoring, anomaly detection and
identification in power electronics and motor-fault detection. Another major
advantage is that a real-time and low-cost hardware implementation is feasible
due to the simple and compact configuration of 1D CNNs that perform only 1D
convolutions (scalar multiplications and additions). This paper presents a
comprehensive review of the general architecture and principals of 1D CNNs
along with their major engineering applications, especially focused on the
recent progress in this field. Their state-of-the-art performance is
highlighted concluding with their unique properties. The benchmark datasets and
the principal 1D CNN software used in those applications are also publically
shared in a dedicated website.

本文综合介绍了 1D CNN 的一般结构和原则、主要的工程应用领域以及这个领域的最新进展和其性能，着重介绍了 1D CNN 的一些独特性质，并公开了其中所使用的基准数据集和主要软件。