Graph data, also known as complex network data, is omnipresent across various domains and applications. Prior graph neural network models primarily focused on extracting task-specific structural features through supervised learning objectives, but they fell short in capturing the inherent semantic and structural features of the entire graph. In this paper, we introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN), which not only models the graph structure but also extracts generalized unsupervised features to enhance vertex classification performance. The SSA-GCN's key contributions lie in three aspects: firstly, it derives semantic information through unsupervised feature extraction from a knowledge graph perspective; secondly, it obtains structural information through unsupervised feature extraction from a complex network perspective; and finally, it integrates these features through a cross-attention mechanism. By leveraging these features, we augment the graph convolutional network, thereby enhancing the model's generalization capabilities. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method. Furthermore, our approach also exhibits excellent accuracy under privacy settings, making it a robust and effective solution for graph data analysis.

翻译：图数据，也称为复杂网络数据，在各领域和应用中无处不在。先前的图神经网络模型主要通过监督学习目标提取任务特定的结构特征，但未能捕捉整个图的固有语义和结构特征。本文提出语义-结构注意力增强图卷积网络（SSA-GCN），该网络不仅对图结构进行建模，还提取广义无监督特征以提升节点分类性能。SSA-GCN的关键贡献体现在三个方面：首先，从知识图谱视角通过无监督特征提取获取语义信息；其次，从复杂网络视角通过无监督特征提取获取结构信息；最后，通过交叉注意力机制融合这些特征。利用这些特征，我们增强图卷积网络，从而提升模型的泛化能力。在Cora和CiteSeer数据集上的实验证明了所提方法的性能提升。此外，该方法在隐私设置下也表现出优异的准确性，成为图数据分析中稳健且有效的解决方案。