  User identity linkage (UIL) across social networks has recently attracted an
increasing amount of attention due to its significant research challenges and
practical value. Most of the existing methods use a single method to express
different types of attribute features. However, the simplex pattern can neither
cover the entire set of different attribute features nor capture the
higher-level semantic features in the attribute text. This paper establishes a
novel semisupervised model, namely the multilevel attribute embedding for
semisupervised user identity linkage (MAUIL), to seek the common user identity
across social networks. MAUIL includes two components: multilevel attribute
embedding and regularized canonical correlation analysis (RCCA)-based linear
projection. Specifically, the text attributes for each network are first
divided into three types: character-level, word-level, and topic-level
attributes. Second, unsupervised approaches are employed to extract the
corresponding three types of text attribute features, and user relationships
are embedded as a complimentary feature. All the resultant features are
combined to form the final representation of each user. Finally, target social
networks are projected into a common correlated space by RCCA with the help of
a small number of prematched user pairs. We demonstrate the superiority of the
proposed method over state-of-the-art methods through extensive experiments on
two real-world datasets.
