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.

通过引入语义 - 结构注意力增强图卷积网络（SSA-GCN），本研究在提取顶点分类性能方面不仅对图结构进行建模，还从无监督特征提取的角度提取通用特征，通过交叉注意力机制整合这些特征，以增强图卷积网络的泛化能力。在 Cora 和 CiteSeer 数据集上的实验证明了我们提出的方法所取得的性能提升，而且在隐私设置下表现出优异的准确性，使其成为图数据分析的坚实有效解决方案。