查看文章

Network embedding is a fundamental part of many network analysis tasks, including node classification and link prediction. The existing random walk-based embedding methods aim to learn node embedding that preserves information on either node proximity or structural similarity. However, the information on both role and community is important to network nodes. To address the shortcomings of the existing methods, this paper proposes a novel method for network embedding called the RARE, which can be used for the analysis of different types of networks and even disconnected networks. The proposed method uses the role and community information of nodes to preserve both node proximity and structural similarity in the learned node embeddings. The walks generated through the role-aware random walk can capture the role and community information of nodes. The obtained walks are input to the Skip …