  Nowadays, users are encouraged to activate across multiple online social
networks simultaneously. Anchor link prediction, which aims to reveal the
correspondence among different accounts of the same user across networks, has
been regarded as a fundamental problem for user profiling, marketing,
cybersecurity, and recommendation. Existing methods mainly address the
prediction problem by utilizing profile, content, or structural features of
users in symmetric ways. However, encouraged by online services, users would
also post asymmetric information across networks, such as geo-locations and
texts. It leads to an emerged challenge in aligning users with asymmetric
information across networks. Instead of similarity evaluation applied in
previous works, we formalize correlation between geo-locations and texts and
propose a novel anchor link prediction framework for matching users across
networks. Moreover, our model can alleviate the label scarcity problem by
introducing external data. Experimental results on real-world datasets show
that our approach outperforms existing methods and achieves state-of-the-art
results.
