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Hundreds of millions of Chinese people have become social network users inrecent years, and aligning the accounts of common Chinese users across multiplesocial networks is valuable to many inter-network applications, e.g.,cross-network recommendation, cross-network link prediction. Many methods haveexplored the proper ways of utilizing account name information into aligningthe common English users' accounts. However, how to properly utilize theaccount name information when aligning the Chinese user accounts remains to bedetailedly studied. In this paper, we firstly discuss the available namingbehavioral models as well as the related features for different types ofChinese account name matchings. Secondly, we propose the framework ofMulti-View Cross-Network User Alignment (MCUA) method, which uses a multi-viewframework to creatively integrate different models to deal with different typesof Chinese account name matchings, and can consider all of the studied featureswhen aligning the Chinese user accounts. Finally, we conduct experiments toprove that MCUA can outperform many existing methods on aligning Chinese useraccounts between Sina Weibo and Twitter. Besides, we also study the bestlearning models and the top-k valuable features of different types of namematchings for MCUA over our experimental data sets.

A Multi-View Approach Based on Naming Behavioral Modeling for Aligning Chinese User Accounts across Multiple Networks