Graph Convolutional Networks (GCNs) achieve great success in non-Euclidean
structure data processing recently. In existing studies, deeper layers are used
in CCNs to extract deeper features of Euclidean structure data. However, for
non-Euclidean structure data, too deep GCNs will confront with problems like
"neighbor explosion" and "over-smoothing", it also cannot be applied to large
datasets. To address these problems, we propose a model called PathSAGE, which
can learn high-order topological information and improve the model's
performance by expanding the receptive field. The model randomly samples paths
starting from the central node and aggregates them by Transformer encoder.
PathSAGE has only one layer of structure to aggregate nodes which avoid those
problems above. The results of evaluation shows that our model achieves
comparable performance with the state-of-the-art models in inductive learning
tasks.

本文介绍一种名为 PathSAGE 的模型，它使用 Transformer 编码器聚合从中心节点开始的路径样本，以学习高阶拓扑信息，提高模型的性能，并避免了太深的 GCNs 可能会遇到的 “邻居爆炸” 和 “过度平滑” 问题，同时在归纳学习任务中达到了与最先进模型相当的性能表现。

PathSAGE: 随机路径采样的空间图注意力神经网络

PathSAGE: Spatial Graph Attention Neural Networks With Random Path Sampling

Existing approaches for graph neural networks commonly suffer from the
oversmoothing issue, regardless of how neighborhoods are aggregated. Most
methods also focus on transductive scenarios for fixed graphs, leading to poor
generalization for unseen graphs. To address these issues, we propose a new
graph neural network that considers both edge-based neighborhood relationships
and node-based entity features, i.e. Graph Entities with Step Mixture via
random walk (GESM). GESM employs a mixture of various steps through random walk
to alleviate the oversmoothing problem, attention to dynamically reflect
interrelations depending on node information, and structure-based
regularization to enhance embedding representation. With intensive experiments,
we show that the proposed GESM achieves state-of-the-art or comparable
performances on eight benchmark graph datasets comprising transductive and
inductive learning tasks. Furthermore, we empirically demonstrate the
significance of considering global information.

提出了一种新的图神经网络 GESM，使用边基邻域关系和节点实体特征的步骤混合，通过随机游走缓解过度平滑问题、动态反映相互关系和基于结构的正则化来增强嵌入表示，实验表明，在包含转导和归纳学习任务的八个基准图数据集上，GESM 取得了最先进或可比较的性能，并证明了考虑全局信息的重要性。