  Hundreds of millions of Chinese people have become social network users in
recent years, and aligning the accounts of common Chinese users across multiple
social networks is valuable to many inter-network applications, e.g.,
cross-network recommendation, cross-network link prediction. Many methods have
explored the proper ways of utilizing account name information into aligning
the common English users' accounts. However, how to properly utilize the
account name information when aligning the Chinese user accounts remains to be
detailedly studied. In this paper, we firstly discuss the available naming
behavioral models as well as the related features for different types of
Chinese account name matchings. Secondly, we propose the framework of
Multi-View Cross-Network User Alignment (MCUA) method, which uses a multi-view
framework to creatively integrate different models to deal with different types
of Chinese account name matchings, and can consider all of the studied features
when aligning the Chinese user accounts. Finally, we conduct experiments to
prove that MCUA can outperform many existing methods on aligning Chinese user
accounts between Sina Weibo and Twitter. Besides, we also study the best
learning models and the top-k valuable features of different types of name
matchings for MCUA over our experimental data sets.
