Hierarchical label with imbalance and attributed network structure fusion for network embedding

Abstract

Network embedding (NE) aims to learn low-dimensional vectors for nodes while preserving the network’s essential properties (e.g., attributes and structure). Previous methods have been proposed to learn node representations with encouraging achievements. Recent research has shown that the hierarchical label has potential value in seeking latent hierarchical structures and learning more effective classification information. Nevertheless, most existing network embedding methods either focus on the network without the hierarchical label, or the learning process of hierarchical structure for labels is separate from the network structure. Learning node embedding with the hierarchical label suffers from two challenges: (1) Fusing hierarchical labels and network is still an arduous task. (2) The data volume imbalance under different hierarchical labels is more noticeable than flat labels. This paper proposes a Hierarchical Label and Attributed Network Structure Fusion model(HANS), which realizes the fusion of hierarchical labels and nodes through attributes and the attention-based fusion module. Particularly, HANS designs a directed hierarchy structure encoder for modeling label dependencies in three directions (parent–child, child–parent, and sibling) to strengthen the co-occurrence information between labels of different frequencies and reduce the impact of the label imbalance. Experiments on real-world datasets demonstrate that the proposed method achieves significantly better performance than the state-of-the-art algorithms.