Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks

arXiv:2403.16033v1 Announce Type: new
Abstract: 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 …

abstract applications arxiv attention classification cs.cl cs.lg cs.si data domains features graph graph data graph neural network network networks neural network node prior semantic supervised learning through type via