  The increasing popularity and diversity of social media sites has encouraged
more and more people to participate in multiple online social networks to enjoy
their services. Each user may create a user identity to represent his or her
unique public figure in every social network. User identity linkage across
online social networks is an emerging task and has attracted increasing
attention, which could potentially impact various domains such as
recommendations and link predictions. The majority of existing work focuses on
mining network proximity or user profile data for discovering user identity
linkages. With the recent advancements in graph neural networks (GNNs), it
provides great potential to advance user identity linkage since users are
connected in social graphs, and learning latent factors of users and items is
the key. However, predicting user identity linkages based on GNNs faces
challenges. For example, the user social graphs encode both \textit{local}
structure such as users' neighborhood signals, and \textit{global} structure
with community properties. To address these challenges simultaneously, in this
paper, we present a novel graph neural network framework ({\m}) for user
identity linkage. In particular, we provide a principled approach to jointly
capture local and global information in the user-user social graph and propose
the framework {\m}, which jointly learning user representations for user
identity linkage. Extensive experiments on real-world datasets demonstrate the
effectiveness of the proposed framework.
