Node Classification via Semantic-Structural Attention-Enhanced Graph
  Convolutional Networks
Hongyin Zhu
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.