  Social network alignment has been an important research problem for social
network analysis in recent years. With the identified shared users across
networks, it will provide researchers with the opportunity to achieve a more
comprehensive understanding of users' social activities both within and across
networks. Social network alignment is a very difficult problem. Besides the
challenges introduced by the network heterogeneity, the network alignment
problem can be reduced to a combinatorial optimization problem with an
extremely large search space. The learning effectiveness and efficiency of
existing alignment models will be degraded significantly as the network size
increases. In this paper, we will focus on studying the scalable heterogeneous
social network alignment problem, and propose to address it with a novel
two-stage network alignment model, namely \textbf{S}calable
\textbf{H}eterogeneous \textbf{N}etwork \textbf{A}lignment (SHNA). Based on a
group of intra- and inter-network meta diagrams, SHNA first partitions the
social networks into a group of sub-networks synergistically. Via the partially
known anchor links, SHNA will extract the partitioned sub-network
correspondence relationships. Instead of aligning the complete input network,
SHNA proposes to identify the anchor links between the matched sub-network
pairs, while those between the unmatched sub-networks will be pruned to
effectively shrink the search space. Extensive experiments have been done to
compare SHNA with the state-of-the-art baseline methods on a real-world aligned
social networks dataset. The experimental results have demonstrated both the
effectiveness and efficiency of the {\our} model in addressing the problem.
