Multi-view learning-based heterogeneous network representation learning

Abstract

Network representation learning is an important tool for extracting latent features from heterogeneous networks to enhance downstream analysis tasks. However, for heterogeneous networks in the era of big data, their heterogeneity, unseen network noises, latent structural and semantic features, and the coupling degree between the two features are increasing. These unignorable changes lead to the difficulties of extracting latent features and the demand for further optimization of existing embedding models. In this paper, an unsupervised multi-view learning based heterogeneous network representation model is proposed, called MVHNR. Firstly, to simplify the difficulty of feature learning, a novel view generation strategy is designed to divide the heterogeneous network into multiple sub-views that contain only one semantic feature and its related structural features. Then, to improve the accuracy and robustness of feature learning with network noises, a novel adjacency matrix strategy is constructed for each view, and a novel adversarial learning strategy is used to both learn the local semantic and structural features in each view. Finally, to aggregate the local features of all views, a multi-headed attention network is used to generate a global feature vector of each node. Extensive experimental results on three tasks and five public networks show the advantages of our MVHNR model.