Role-aware random walk for network embedding

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-gram model to learn the final embedding of nodes. In addition, the RARE is extended to the CRARE that adds the sampling of high-order community members to the customized random walk so that the node's representation can preserve more structural information of the network. The performances of the proposed methods are evaluated on multi-class node classification, link prediction, and network visualization tasks. Experimental results on different domain datasets indicate that the proposed methods outperform the baseline methods. The proposed methods can be further accelerated using parallelization in the random walk generation process. The source code: https://github. com/HeguiZhang/RARE.

Automated summary

The proposed RARE method utilizes node role and community information to preserve both node proximity and structural similarity in learned embeddings. Through role-aware random walk and Skip-gram model, node embedding learning is achieved. The CRARE method extends RARE by sampling high-order community members to preserve more structural information of the network.