In recent years, graph neural networks (GNNs) have become a popular tool for
solving various problems over graphs. In these models, the link structure of
the graph is typically exploited and nodes' embeddings are iteratively updated
based on adjacent nodes. Nodes' contents are used solely in the form of feature
vectors, served as nodes' first-layer embeddings. However, the filters or
convolutions, applied during iterations/layers to these initial embeddings lead
to their impact diminish and contribute insignificantly to the final
embeddings. In order to address this issue, in this paper we propose augmenting
nodes' embeddings by embeddings generating from their content, at higher GNN
layers. More precisely, we propose models wherein a structural embedding using
a GNN and a content embedding are computed for each node. These two are
combined using a combination layer to form the embedding of a node at a given
layer. We suggest methods such as using an auto-encoder or building a content
graph, to generate content embeddings. In the end, by conducting experiments
over several real-world datasets, we demonstrate the high accuracy and
performance of our models.

本文中，我们提出了在高级 GNN 层次中通过内容生成的嵌入来增强节点嵌入，实验证明我们的模型具有高准确性和性能。

内容增强图神经网络

Content Augmented Graph Neural Networks

Graph Neural Networks (GNNs) have risen to prominence in learning
representations for graph structured data. A single GNN layer typically
consists of a feature transformation and a feature aggregation operation. The
former normally uses feed-forward networks to transform features, while the
latter aggregates the transformed features over the graph. Numerous recent
works have proposed GNN models with different designs in the aggregation
operation. In this work, we establish mathematically that the aggregation
processes in a group of representative GNN models including GCN, GAT, PPNP, and
APPNP can be regarded as (approximately) solving a graph denoising problem with
a smoothness assumption. Such a unified view across GNNs not only provides a
new perspective to understand a variety of aggregation operations but also
enables us to develop a unified graph neural network framework UGNN. To
demonstrate its promising potential, we instantiate a novel GNN model,
ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across
nodes. Comprehensive experiments show the effectiveness of ADA-UGNN.

本篇论文针对图结构数据的表征学习中的聚合操作进行研究，探究 GCN、GAT、PPNP、APPNP 等 GNN 模型的聚合过程与图像去噪问题之间的通用性，进而提出统一图神经网络框架 UGNN，并基于此开发了 ADA-UGNN 模型，通过实验验证了其在自适应平滑性的图中的有效性。