Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding

Nowadays, it is common for a person to possess different identities on multiple social platforms. Social network alignment aims to match the identities that from different networks. Recently, unsupervised network alignment methods have received significant attention since no identity anchor is required. However, to capture the relevance between identities, the existing unsupervised methods generally rely heavily on user profiles, which is unobtainable and unreliable in real-world scenarios. In this paper, we propose an unsupervised alignment framework named Large-Scale Network Alignment (LSNA) to integrate the network information and reduce the requirement on user profile. The embedding module of LSNA, named Cross Network Embedding Model (CNEM), aims to integrate the topology information and the network correlation to simultaneously guide the embedding process. Moreover, in order to adapt LSNA to large-scale networks, we propose a network disassembling strategy to divide the costly large-scale network alignment problem into multiple executable sub-problems. The proposed method is evaluated over multiple real-world social network datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.