Iterative Deep Graph Learning with Local Feature Augmentation for Network Alignment

Jiuyang Tang, Zhen Tan, Hao Guo, Xuqian Huang, Weixin Zeng, Huang Peng

Abstract: Networks are structures that naturally capture relations between entities in different data sources and information systems. To establish the connections among different networks, the task of network alignment is proposed and intensively studied in network-related research field. Most network alignment methods are based on the representation learning of network structure, which rely only on network topology and are susceptible to structural noise. In addition, such methods focus on the global features, while largely neglect local structure features and fail to take account of the data sparsity issue in real networks. To address these pivotal issues, we propose a novel network alignment method based on iterative deep network learning and local feature augmentation. We first design an iterative deep graph learning model to learn high-quality network structural representation and reduce the structural noise. Furthermore, we embed knowledge representation learning method into the alignment process, which helps to characterize better local structure and alleviate the data sparsity issue. Experiments on real-world network datasets demonstrate that our proposed model achieves state-of-the-art alignment results.