Common and Unique Features Learning in Multi-view Network Embedding

Network embedding is a powerful representation learning method for graph data, using the learned low-dimensional compact vectors as node features, which are widely used in various tasks, such as link prediction, node clustering, and classification. Compared with traditional network analysis methods, network embedding reduces computational complexity and improves analysis efficiency. Although previous work has achieved outstanding performance, it faces challenges in multi-view network embedding containing multi-type node relations. Since multi-view networks share a node set but different edges, different networks not only have common information but also have their unique information. To simultaneously capture multi-view networks' common and unique information, we propose a new framework, Common and Unique Features Learning for Multi-view Network Embedding (CU-MNE), to integrate multi-type node relations. In this paper, we propose an inter-view contrastive objective to ensure the consistency of the common features of the same node in a different view and an interfeature contrastive objective to capture the association between the common and unique features of each network node that can learn high-quality node embeddings. Extensive experiments on three real datasets show that CU-MNE outperforms the state-of-the-art methods.