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.

翻译：近年来，图神经网络（GNN）已成为解决各种图结构问题的流行工具。在这类模型中，通常利用图的链接结构，并基于相邻节点迭代更新节点嵌入。节点内容仅以特征向量的形式使用，作为节点的第一层嵌入。然而，在迭代/层中应用于这些初始嵌入的滤波器或卷积会导致其影响逐渐减弱，对最终嵌入的贡献微乎其微。为解决这一问题，本文提出在更高GNN层中，通过从节点内容生成嵌入来增强节点嵌入。具体而言，我们提出了若干模型，其中为每个节点计算基于GNN的结构嵌入和内容嵌入，并通过组合层将两者结合，形成指定层的节点嵌入。我们提出了使用自编码器或构建内容图等方法生成内容嵌入。最后，通过在多个真实世界数据集上的实验，我们证明了所提模型的高准确性与高性能。