Role-Oriented Network Embedding Method Based on Local Structural Feature and Commonality

Abstract

Role-oriented network embedding has become a powerful technique for solving real-world problems, because it can capture the structures of nodes and make node embeddings better reflect the functions or behaviors of entities in the network. At present, various role-oriented network embedding methods have been proposed. However, most of the methods ignore degree distribution and the commonalities among local structures, resulting in insufficient information of node embeddings, and some methods that preserve commonality always have high time complexity. To address the above challenges, we propose a novel model ReVaC from two aspects of extracting higher-quality local structural features and strengthening the commonalities among local structures in node embeddings. In detail, the degree distribution from node’s 1-hop egonet is incorporated into the extraction process of local structural features to improve traditional ReFeX firstly, and the Variational Auto-Encoder is used to map those features to the local structural embedding space. Then, in the embedding space, we cluster nodes to model the commonalities among local structures. Finally, local structural embeddings and commonalities are fused to get node embeddings. We conduct extensive comparative experiments on real-word networks, and results show that ReVaC has better performance than other state-of-the-art approaches and adapts well to network scale.