  Social network alignment aims at aligning person identities across social
networks. Embedding based models have been shown effective for the alignment
where the structural proximity preserving objective is typically adopted for
the model training. With the observation that ``overly-close'' user embeddings
are unavoidable for such models causing alignment inaccuracy, we propose a
novel learning framework which tries to enforce the resulting embeddings to be
more widely apart among the users via the introduction of carefully implanted
pseudo anchors. We further proposed a meta-learning algorithm to guide the
updating of the pseudo anchor embeddings during the learning process. The
proposed intervention via the use of pseudo anchors and meta-learning allows
the learning framework to be applicable to a wide spectrum of network alignment
methods. We have incorporated the proposed learning framework into several
state-of-the-art models. Our experimental results demonstrate its efficacy
where the methods with the pseudo anchors implanted can outperform their
counterparts without pseudo anchors by a fairly large margin, especially when
there only exist very few labeled anchors.
