Self-SAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

Abstract

Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks.